CA2555280A1 - Semantic knowledge retrieval management and presentation - Google Patents

Abstract

The present invention is directed to an integrated implementation framework and resulting medium for knowledge retrieval, management, delivery and presentation. The system includes a first server component that is responsible for adding and maintaining domain- specific semantic information (item 50) and a second server component (iem 80) that hosts semantic and other knowledge for use by the first server component that work together to provide text and time-sensitive semantic information retrieval services to clients operating a presentation platform via a communication medium (item 10). Within the system, all objects or events given hierarchy are active Agents (item 90) semantically related to each other and representing queries (comprised of underlying action code) that return data objects for presentation to the client according to a predetermined and customizable theme or ~Skin~. This system provides various means for the client to customize and ~blend~ Agents and the underlying related queries to optimize the presentation of the resulting information (item 30).

Description

DEMANDE OU BREVET VOLUMINEUX
LA PRESENTE PARTIE DE CETTE DEMANDE OU CE BREVET COMPREND
PLUS D'UN TOME.

NOTE : Pour les tomes additionels, veuillez contacter 1e Bureau canadien des brevets JUMBO APPLICATIONS/PATENTS
THIS SECTION OF THE APPLICATION/PATENT CONTAINS MORE THAN ONE
VOLUME

NOTE: For additional volumes, please contact the Canadian Patent Office NOM DU FICHIER / FILE NAME
NOTE POUR LE TOME / VOLUME NOTE:

SYSTEM AND METHOD FOR SEMANTIC KNOWLEDGE RETRIEVAL, MANAGEMENT, CAPTURE, SHARING, DISCOVERY, DELIVERY AND
PRESENTATION
INVENT~R
Nosa Omoigui PRI~RITY CLAIM
This application is a C~ntmuation-In-Part of U.S. Application Serial No.
10/179,651 filed June 24, 2002, which claims priority to U.S. Provisional Application No.
60/360,610 filed February 2S, 2002 and to U.S. Provisional Application No. 60/300,355 filed June 22, 2001. This Application also claims priority to U.S. Provisional Application No.60/447,736 filed February 14, 2003. This Application also claims priority to PCTICTS02/20249 filed June 24, 2002. All of the foregoing applications are hereby incorporated by reference in their entirety as if fully set forth herein.
COPYRIGHT NOTICE
This disclosure is protected under United States and International Copyright Laws.
2002 - 2004 Nosa Omoigui. All Rights Reserved. A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
FIELD OF THE INVENTION
This invention relates generally to computers and, more specifically, to information management and research systems.
BACKGROUND OF THE INVENTION
The general baclcground to this invention is described in my co-pending parent application (U.S. Application Serial No. 10/179,651 filed June 24, 2002), which is incorporated by reference herein, and of which this application is a Continuation in Part.
SUMMARY OF THE INVENTION
The present invention is directed in part to a semantically integrated knowledge retrieval, management, delivery and presentation system, as is more fully described in my co-pending parent application (U.S. Application Serial No. 10/179,651 filed June 24, 2002). The present invention and system includes several additional improved features, enhancements andlor properties, including, without limitation, Entities, Profiles and Semantic Threads, as are more fully described in the Detailed Description below.
BRIEF DESCRIPTION OF THE DRAWINGS
The preferred and alternative embodiments of the present invention are described in detail below with reference to the following drawings.
FIGURE 1 is a partial screenshot overview and FIGURE 2 is an expansion of a dialog box of FIGURE 1 for a scenario of a Patent Examiner using the preferred embodiment in a prior art search, a screenshot of where "Magnetic Resonance Imaging" occurs in a Pharmaceuticals taxonomy.

FIGURE 3 shows the Sharable Smart Request System Interaction, which is the binary document format that encapsulates the SQML buffer with the smart request and also illustrates how the extension handler opens a document.
FIGURE 4A is a partial screenshot overview of document files.
FIGURE 4B shows an illustration of two .REQ documents from FIGURE 4A (titled 'Headlines on Reuters Related to My Research Report (Live)' and 'Headlines on Reuters (as of January 21 2003, 08 17AM)' on the far right) With a registered association in the Windows shell.
FIGURE 5 is a Diagram Illustrating the Text-to-Speech Object Skin and shows an illustration of an email message being rendered via a text-to-speech object skin.
FIGURE 6 is a Diagram Illustrating a Text-to-Speech Request Skin.
FIGURE 7 is a Diagram Illustrating Knowledge Modeling for a Pharmaceuticals Company Example.
FIGURE 8 is a Diagram Illustrating Client Component Integration and Interaction Workflow.
FIGURES 9 -11 show three different views of the Explore Categories dialog box.
FIGURES 12 and 13 show sample screenshots of the Dossier Smart Lens in operation.
FIGlJRE 14 shows how the server-side semantic query processor processes incoming semantic queries (represented as SQML).
FIGURE 15 illustrates the semantic browser showing two profiles (the default profile named "My Profile" and a profile named "Patents"). Observe how the user is able to navigate his/her knowledge worlds via both profiles without interference.
FIGURE 16A-C illustrate how a user would configure a profile (to create a profile, the user will use the "Create Profile Wizard" and the profile can then be modified via a property sheet as shown in other Figures).
FIGURE 17 shows how a user would select a profile when creating a request with the "Create Request Wizard."

FIGURE 18 shows a screenshot with the 'Smart Styles' Dialog Box illustrating some of the foregoing operations and features.
FIGURE 19 illustrates the "Smart Request Watch" Dialog Box.
FIGURE 20 illustrates a Watch Window displaying Filtered Smart Requests (e.g., Headlines on Wireless). Figure 20 is an Illustration of the Watch Window with a Current Smart Request Title (e.g., "Breaking News").
FIGURE 21 illustrates Entity views displayed in the Semantic Browser.
FIGURE 22A and 22B show the UI for the Knowledge Community Subscription.
FIGURE 23 illustrates a semantic thread object and its semantic links.
FIGURES 24 through 46B are additional screen shots further illustrating the functions, options and operations as described in the Detailed Description.
FIGURE 47 as a sample semantic image for Pharmaceuticals/Biotech industry (DNA
helix).
FIGURE 48 is an illustration of a semantically appropriate image visualization for the Breaking News context template.
FIGURE 49 is a Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Headlines).
FIGURE 50 is a Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Two people working at a desk).
FIGURE 51 illustrates a semantic "Newsmaker" Visualization or Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURE 52 illustrates a semantic "Upcoming Events" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURE 53 is a Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Petri Dish).
FIGURE 54 illustrates a semantic "History" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.

FIGURE 55 illustrates a semantic Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Spacecraft).
FIGURE 56 illustrates a "Best Buys" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURE 57 illustrates a semantic Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Coffee).
FIGURE 58 illustrates a semantically appropriate Sample Image for "Classics"
for smart hourglass, interstitial page, transition effects, background chrome, etc.
(Car).
FIGURE 59 illustrates a semantically appropriate "Recommendation"
Visualization -Sample Image for the contextual/application elements of smart hourglass, interstitial page, transition effects, background chrome, etc. (Thumbs up).
FIGURE 60 illustrates a semantic "Today" Visualization - Sample Image for the elements smart hourglass, interstitial page, transition effectsa background chrome, etc.
FIGURE 61 illustrates a semantic "Annotated Items" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURE 62 illustrates a semantic "Annotations" Visualization - Sample hnage for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURE 63 illustrates a semantic "Experts" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURE 64 illustrates a semantic "Places" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURE 65 illustrates a semantic "Blenders" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
FIGURES 66 through 84 illustrate semantic Visualizations for the following Information Object Types, respectively: Documents, Books, Magazines, Presentations, Resumes, Spreadsheets, Text, Web pages, White Papers, Email, Email Annotations, Email Distribution Lists, Events, Meetings, Multimedia, Online Courses, People, Customers, and Users.

Figure 85 illustrates a semantic "Timeline" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc..

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
TABLE OF CONTENTS
A. ADDITIONAL ILLUSTRATIVE SCENARIOS
...............................................................................
.................9 1. Patent Examiner Prior Art Search Tool...........................................................................
................................9 2. BioTech Company Research Scenario.......................................................................
...................................13 B. SUBJECT MATTER FOR THE PRESENTLY PREFERRED EMBODIMENT OF THE INFORMATION
NERVOUS
SYSTEM.........................................................................
...............................................................17 1. Smart Selection Lens Overview.............................................................,.........
.............................................18 2. Pasting Person Objects Overview ...............................................................................
..................................20 3. Saving and Sharing Smart Requests Overview.......................................................................
......................22 4. Saving and Sharing Smart Snapshots Overview ...............................................................................
............24 5. Virtual Knowledge Communities....................................................................
..............................................25 6. Implementing Time-Sensitive Semantic Queries........................................................................
..................26 7. Text-To-Speech Skins Overview ...............................................................................
...................................26 8. Language Translation Skins..........................................................................
................................................28 9. Categories as First Class Objects in the User Experience.....................................................................
........29 10. Categorized Annotations ...............................................................................
................................................ 29 11. Additional Context Templates ...............................................................................
.......................,...............30 12. Importing and Exporting User State.............................................................,............
....................................31 13. Local Smart Requests.......................................:.............................., ......,......................................................32 14. Integrated Navigation.....................................................................
.........:.....................................................32 15. Hints for Visited Results .........................................................................:.....
................................................33 16. Knowledge Federation ...............................................................................
...................................................34 17. Anonymous Annotations and Publications ...............................................................................
....................38 18. Offline Support iii the Semantic Browser ...............................................................................
......................38 19. Guaranteed Cross-Platform Support in the Semantic Browser .....................................................................39 20. Knowledge Modeling.....,.................................................................
.............................................................40 21. KIS Housekeeping Rules ...............................................................................
...............................................41 22. Client Component Integration & Interaction Workflow ...............................................................................

23. Categories Dialog Box User Interface Specification..................................................................
...................42 24~. Client-Assisted Server Data Consistency Checking..............................,........................................
...............45 25. Client-Side Duplicate Detection......................................................................
..............................................4~6 26. Client-Side Virtual Results Cursor.........................................................................
.......................................46 27. Virtual Single Sign-On.............................................................................
.....................................................47 28. Namespace Object Action Matrix.........................................................................
........................................49 29. Dynamic End-to-End Ontology/Taxonomy Updating and Synchronization................................................. 50 30. Invoking Dossier (Guide) Queries ...............................................................................
.................................51 31. Knowledge Community (Agency) Semantics ........................................,......................................
................ 52 32. Dynamic Ontology and Taxonomy Mapping........................................................................
............,...........53 33. Semantic Alerts Optimizations..................................................................
....................................................53 a »

34. Semantic 'News Images.........................................................................
.....................................................54 35. Dynamically Choosing Semantic Images.........................................,...............................
.............................54 36. Dynamic Knowledge Community (Agency) Contacts Membership.............................................................54 37. Integrated Full-Text Keyword and Phrase Indexing ........................,......................................................
......55 38. Semantic "Mark Object as Read".....................................,....................................
........................................56 39. Mufti-Select Object Lens ...............................................................................
...............................................57 40. Ontology-Based Filtering and Spam Management ...............................................................................
........ 58 41. Results Refmement ...............................................................................
.,...................................................... 58 42. Semantic Management of Information Stores........................................,................................
......................60 43. Slide-Rule Filter User Interface ...............................................................................
..................................... 61 C. SERVER-SIDE SEMANTIC QUERY PROCESSOR
SPECIFICATION........................................................62 1.
Overview.......................................................................
...............................................................................
. 62 2. Semantic Relevance Score ...............................................................................
............................................. 63 3. Semantic Relevance Filter.........................................................................
....................................................63 4. Time-Sensitivity Filter ...............................................................................
................................................... 64 5. Knowledge Type Semantic Query Implementations................................................................
.....................64 D. EXTENSIBLE CLIENT-SIDE USER PROFILES SPECIFICATION FOR THE
INFORMATION NERVOUS

SYSTEM
...............................................................................
............................................................................

E. SMART STYLES SPECIFICATION FOR THE INFORMATION NERVOUS SYSTEM..73 ...........................

1. Smart Styles Overview.......................................................................
...........................................................73 2. Implicit and Dynamic Smart Style Properties....,................................................................
..........................73 F. SMART REQUEST WATCH SPECIFICATION FOR THE INFORMATION NERVOUS..75 SYSTEM ........

1.
Overview.......................................................................
...............................................................................
.

2. Request Watch Lists (RWLs) and Groups (RWGs).........................................................................
.............76 3. The Notification Manager (NM) ...........................................................,...................
....................................79 4. Watch Group Monitors ...............................................................................
.....,............................................80 5. The Watch Pane ...............................................................................
.............................................................

6. The Watch Window ...............................................................................
.......................................................

7. Watch List Addendum ...........................................................,...................
...................................................82 G. ENTITIES SPECIFICATION FOR THE INFORMATION NERVOUS
SYSTEM.........................................

1.
Introduction...................................................................
...............................................................................
.

2. Portfolios (or Entity Collections) ...............................................................................
..........,........................88 3. Sample Scenarios ...............................................................................
...............:...........................................

H. KNOWLEDGE COMMUNITY BROWSING AND SUBSCRIPTION SPECIFICATION
FOR THE

INFORMATION NERVOUS
SYSTEM.........................................................................
..................................

I. CLIENT-SIDE SEMANTIC QUERY DOCUMENT SPECIFICATION FOR THE
INFORMATION

NERVOUS
SYSTEM.........................................................................
...............................................................91 1. Semantic Query Markup Language (SQML) Overview ...............................................................................

2. SQML
Generation.....................................................................
..................................................................100 3. SQML
Parsing........................................................................
.....................................................................100 J. SEMANTIC CLIENT-SIDE RUNTIME CONTROL API SPECIFICATION FOR
THE INFORMATION

NERVOUS
SYSTEM.........................................................................
......,......................................................101 1. Introducing the Nervana Semantic Runtime Control - Overview 101 ..................................................,............

2. The Nervana Semantic Runtime Control API............................................................................
.................101 3. Email Control APIs............................,..........................,...................
.............................................,............112 4. Person Control APIs...........................................................................
.........................................................115 5. System Control Events.........................................................................
.......................................................118 I~.SECURITY SPECIFICATION FOR THE INFORMATION NERVOUS
SYSTEM.~I.....................................120 1. Authorization ...............................................................................
...............................................................120 2. People Groups.........................................................................
...............,....................................................124 3. Identity Metadata Federation ...............................................................................
.......................................125 4. Access Control........................................................................
............................................,.......................126 L. DEEP INFORMATION SPECIFICATION FOR THE INFORMATION NERVOUS 132 SYSTEM..................

M. CREATE REQUEST WIZARD SPECIFICATION FOR THE INFORMATION NERVOUS138 SYSTEM .....

N. CREATE PROFILE WIZARD SPECIFICATION FOR THE INFORMATION NERVOUS140 SYSTEM.......

O. CREATE BOOI~MMARK WIZARD SPECIFICATION FOR THE INFORMATION 141 NERVOUS SYSTEM

1. Introducing the Create Bookmark Wizard ...............................................................................
...................141 2. Scenarios ...............................................................................
......................................................................142 3. Intelligent Publishing-Tool Metadata Suggestion and Maintenance...........................................................142 P. SEMANTIC THREADS SPECIFICATION FOR THE INFORMATION NERVOUS 143 SYSTEMTM.............

1. Semantic Threads.............................:.........~................................
................................................................143 2. Semantic Thread Conversations..................................................................
..........................................,.....146 3. Semantic Thread Management................................................................:....
...............................................147 Q. SAMPLE SCREEN
SHOTS.......................................................................,..
..................................................148 R. SPECIFICATION FOR SEMANTIC QUERY DEFII~1ITIONS & VISUALIZATIONS
FOR THE

INFORMATION NERVOUS
SYSTEM.........................................................................
................................148 1. Semantic Images & Motion ...............................................................................
.........................................148 2. The Smart Hourglass......................................................................
.............................................................153 3. Visualizations -- Context Templates ...............................................................................
............,...............154 In a currently preferred embodiment, the system incorporates not only the features and functions described in my parent application and this CIP.
A. ADDITIONAL ILLUSTRATIVE SCENARIOS
The following scenarios help to explain the utility and operation of the system, and will thereby make the rest of the detailed description easier to follow and understand.
1. Patent Examiner Prior Art Search Tool Largely because of PTO fee diversion, there is a great' deal of pressure on U.S. Patent Examiners to conduct a robust prior art search in very little time. And, while the research tools available to Examiners have improved dramatically in the last several years, those tools still have many shortcomings. Among the shortcomings are that most of the research tools are text based, rather than meaning based. So, for example, the search tool on the PTO website will search for particular words in particular fields in a document. Similarly, the advanced search tool on Google enables the Examiner to locate documents with particular words, or particular strings of words, or documents without a particular word or words. However, in each case, the search engine does not allow the Examiner to locate documents on the basis of meaning. So, for example, if there is a relevant reference that teaches essentially the same ideaq but uses completely different words (e.g., a synonym, or worse yet, a synonymous phrase) than those in the query, the reference, even though perhaps anticipating, may well not be discovered. Even if the Examiner could spare the time to imagine and search every possible synonym, or even synonymous phrase to the key words critical to the invention, it could still overlook references because sometimes the same idea can be expressed without using any of the same words at all, and sometimes the synonymous idea is not neatly compressed into a phrase, but distributed over several sentences or paragraphs.
The reason for this is that words do not denote or connote meaning one to one as, for example, numerals tend to do. Put differently, certain meanings can be denoted or connoted by several different words or an essentially infinite combination of words, and, conversely, certain words or combinations of words can denote or connote several different meanings. Despite this infinite many-to-many network of possibilities human beings can isolate (because of context, experience, reasoning, inference, deduction, judgment, learning and the like) isolate probable meanings, at least tolerably effectively most of the time. The current prior art computer-automated search tools (e.g. the PTO website, or Google, or Lexis), cannot.
The presently preferred embodiment of my invention bridges this gap considerably because it can search on the basis of meaning.
For example, using the some of the search functions of the preferred embodiment of the present invention, the Examiner could conduct a search, and with no additional effort or time as presently invested, obtain search results relevant to patentability even if they did not contain a single word in common with the key words chosen by the Examiner. Therefore, the system would obtain results relevant to the Examiner's task that would not ordinarily be located by present systems because it can locate references on the basis of meaning.
Also on the basis of meaning, it can exclude irrelevant references, even if they share a key word or words in common with the search request. In other words, one problem in prior art research is the problem of a false positive; results that the search engine "thought" were relevant merely because theg~ had a key v~ord in conunon, but that were in fact totally irrelevant because the key word, upon closer inspection in context, actually denoted or connoted an irrelevant idea.
Therefore, the Examiner must search for the needle in the haystack, which is a waste of time.
W contrast, using some of the search functions of the preferred embodiment of the present invention, the density of relevant search results increases dramatically, because the system is "intelligent" enough to omit search results that, despite the common key words, are not relevant. Of course, it is not perfect in this respect any more than human beings are perfect in this respect. But, it is much more effective at screening irrelevant results than present systems, and in this respect resembles in function or in practice an intelligent research assistant than a mere keyword based search engine. Thus, using the system, the Examiner can complete a much better search in much less time. The specific mechanics of using the system this way, in one example, would work as follows:
Imagine the Examiner is assigned to examine an application directed to computer software for a more accurate method of interpreting magnetic resonance data and thereby generating more accurate diagnostic images. To search for relevant prior art using the search fiuzctions of the preferred embodiment of the present invention, the Examiner would:
a. Using the Create Entity wizard, create a "Topic" entity with the relevant categories in the various contexts in which "Magnetic Resonance Imaging"
occurs. As an illustration, Figures 1 and 2 show where "Magnetic Resonance Imaging" occurs in a Pharmaceuticals taxonomy. Notice that there are several contexts in which the category occurs.
Add the relevant categories to the entity and apply the "OR" operation.
Essentially, this amounts to defining the entity "Magnetic Resonance Imaging" (as it relates to YOUR
specific task) as being equivalent to all the occurrences of Magnetic Resonance Zinaging in the right contexts -based on the patent application being examined.
b. Name the new entity "Magnetic Resonance Imaging" and perhaps "imaging" and "diagnostic" or some variations and combinations of the same.
c. Drag and drop the "Magnetic Resonance Imaging" Topic entity to the Dossier (special agent or default knowledge request) icon in the desired profile (the profile is preferably configured to include the "Patent Database" knowledge community). This launches a new Dossier requestlagent that displays each special agent (context template).
Each special agent is displayed with the right default predicate as follows:
~ All Bets on Magnetic Resonance Imaging ~ Best Bets on Magnetic Resonance Imaging ~ Breaking News on Magnetic Resonance Imaging ~ Headlines on Magnetic Resonance Imaging ~ Random Bets on Magnetic Resonance Imaging ~ Experts in Magnetic Resonance Imaging ~ Newsmakers in Magnetic Resonance Imaging ~ Interest Group in Magnetic Resonance Imaging ~ Conversations on Magnetic Resonance hnaging ~ Annotations on Magnetic Resonance Imaging ~ Annotated Items on Magnetic Resonance Imaging ~ Upcoming Events on Magnetic Resonance Imaging ~ Popular Items on Magnetic Resonance Imaging ~ Classics on Magnetic Resonance Imaging d. Alternatively, the request can be created by using the Create Request Wizard. To .do this, select the Dossier context template and select the "Patent Database"
knowledge community as the knowledge source for the request. Alternatively, you can configure the profile to include the "Patents Database" knowledge community and simply use the selected profile for the new request. Hit Next - the wizard intelligently suggests a name for the request based on the semantics of the request. The wizard also selects the right default predicates based on the semantics of the "Magnetic Resonance hnaging" "Topic" entity. Because the wizard knows the entity is a "Topic," it selects the right entities that make sense in the right contexts. Hit Finish.
The wizard compiles the query, sends the SQML to the I~ISes in the selected profile, and then displays the results.
In the foregoing example, the results could be drawn, ultimately, from any source.
Preferably, some of the results would have originated on the Web, some on the PTO intranet, some on other perhaps proprietary extranets. Regardless of the scope or origin of the original documents, bg~ use of the system they hare been automatically processed, and automatically "read" and "understood" by the system, so that when the Examiner's query was initiated, and also "read" and "understood" semaaztically, and by context, the system locates all relevant, and only relevant results. Again, not perfectly, but radically more accurately than in any prior systems. Note also that the system does not depend on any manual tagging or categorization of the documents in advance. While that would also aid in accuracy, it is so labor intensive as to utterly eclipse the advaaZtages of online research in the first place, and is perfectly impractical given the rate of increase of new documents.
W this scenario, the Examiner may also wish to use additional features of the preferred embodiment of the invention. For example, the Examiner may wish to consult experts within the PTO, or literature by experts outside the PTO, as follows (note that Experts in Magnetic Resonance Imaging would be included in the Dossier on Magnetic Resonance Imaging;
however, the examiner might want to create a separate request for Experts in order to track it separately, save it as a "request document," email it to colleagues, etc.).
Find all Experts in Magnetic Resonance Imaging:
a. Follow steps 1-4 above.
b. Drag and drop the "Magnetic Resonance Imaging" entity to the Experts (special agent or default knowledge request) icon in the desired profile. This automatically launches a new requestlagent appropriately titled "Experts in Mag~letic Resonance Imaging." The semantic browser selects the xight default predicate "in" because it "knows" the entity is a "Topic" entity and the context template is a "People" template (Experts). As such, the default predicate is selected based on the intersection of these two arguments ("in") since this is what makes sense.
2. BioTech Company Research Scenario Eiotech companies are research intensive, not only in laboratory research, but in research of the results of research by othexs, both within and outside of their own companies.
Unfortunately, the research tools available to such companies have shortcomings. Proprietary services provide context-sensitive and useful results, but those services themselves have inferior tools, and thus rely heavily on indexing and human effort, and subscriptions to expensive specialized joun~als, and as consequence are very expensive and not as accurate as the present system. ~n the other hand, biotech researchers can search inexpensively using Google0, but it shares all the lcey word based limitations described above.
In contrast, using the search features of the preferred embodiment of the present invention, a biotech researcher could more efficiently locate more relevant results. Specifically, the researcher might use the system as follows. For example, if some researchers wanted to Find Headlines on Genomics and .Anatomy written by anyone in Marketing or Research, they would do that as follows:

a. Using the wizard, launch an information-type requestlagent for distribution lists with the keywords "Marketing Research".
b. Select the Marketing distribution list result and click "Save as Entity" -this saves the object as a "Team" entity (because the semantic browser "knows" the original object is a distribution list - as such, a "Team" entity makes sense in this context).
c. Select the Research distribution list result and click "Save as Entity" -this saves the object as a "Team" entity (because the semantic browser "knows" the original object is a distribution list).
d. Using the Create Entity Wizard, create a new "Team" entity and select the "Marketing" and "Research" team entities as members. Name the new entity "Marketing or Research".
e. Using the Create Request Wizard, select the Headlines context template, and then select the "Marketing or Research" entity as a filter. Also, seleet the Genomics category and the Anatomy category. Next, select the "AND" operator. Hit Next - the wizard intelligently suggests a name for the request based on the semantics of the request. The wizard also selects the right default predicates based on the semantics of the "Marketing or Research"
team entity ("by an~rone in'°). Eecause the wizard kno~rs the entity is a "Team," it selects "by anyone in" by default since this makes sense. Hit Finish. The wizard compiles the query, sends the SQML to the I~ISes in the selected profile, and then displays the results.
In addition, the researchers may wish to Find all Experts in Marketing or Research:
a. Follow steps 1-4 above.
b. Drag and drop the "Marketing or Research" entity to the Experts (special agent or default knowledge request) icon in the desired profile. This launches a new request/agent appropriately titled "Experts in Marketing or Research." The semantic browser selects the right default predicate "in" because it "lcnows" the entity is a "Team" entity and the context template is a "People" template (Experts). As such, the default predicate is selected based on the intersection of these two arguments ("in") since this is what makes sense.

If the researchers expect to need to return to this research, or to supplement it, or to later analyze the results, they may wish to Open a Dossier on Marketing or Research, as follows:
a. Follow steps 1-4 above.
b. Drag and drop the "Marketing or Research" entity to the Dossier (special agent or default knowledge request) icon in the desired profile. This launches a new Dossier requestlagent that displays each special agent (context template). Each special agent is displayed with the right default predicate as follows:
~ All Bets by anyone in Marketing or Research ~ Best Bets by anyone in Marketing or Research ~ Breaking News by anyone in Marketing or Research ~ Headlines by anyone in Marketing or Research ~ Random Bets by anyone in Marketing or Research ~ Experts in Marketing or Research ~ Newsmakers in Marketing or Research ~ Interest Group in Marketing or Research ~ Conversations involving anyone in Marketing or Research Annotations by anyone in Marketing or Research Annotated Items by anyone in Marketing or Research ~ Upcoming Events by anyone in Marketing or Research ~ Popular Items by anyone in Marketing or Research ~ Classics by anyone in Marketing or Research 'The researchers may be interested in Finding egBrealbing Ne~~rs on my ~'~mpetitors'q, and would do so as follows:
a. For each competitor, create a new "competitor" entity (under "companies") using the Create Entity Wizard. Select the right filters as needed. For instance, a competitor with a well-known English name - like "Groove" should have an entity that includes categories in which the company does business and also the keyword.
b. Using the Create Entity Wizard, create a portfolio (entity collection) and add all the competitor entities you created in step a. Name the entity collection "My Competitors."
c. Using the Create Request Wizard, select the Breaking News context template and add the portfolio (entity collection) you created in step b. as a filter. Keep the default predicate selection. Hit "Next" - the wizard intelligently suggests a name for the request using the default predicate ("Breaking News on My Competitors"). Hit Finish. The wizard launches a new request/agent named "Breaking News on My Competitors."
In addition, the researchers may wish to be kept apprised. They could instruct the system to alert them on "Breaking News on our Competitors", as follows:
a. Create the "Breaking News on My Competitors" request as described above.
b. Add the request to the request watch list. The semantic browser will now display a watch pane (e.g., a ticker) showing "Breaking News on My Competitors." Using the Notification Manager (NM), you can also indicate that the semantic browser send alerts via email, instant messaging, text messaging, etc. when there are new results from the request/agent.
In addition, the researchers may wish to keep records of competitors for future reference, and to have them constantly updated. The system will create and update such records, by the researchers instructing the system to Show a collection of Dossiers on each of our competitors, as follows:
a. Create entities for each of your competitors as described in 4a. above.
b. For each competitor entity, create a new Dossier on that competitor by dragging the entity to the Dossier icon for the desired profile - this creates a Dossier on the competitor.
c. Using the Create Request ~Nizard, create a new request collection (blender) and add each of the Dossier requests created in step b. above to the collection (you can also drag and drop requests to the collection after it has been created in order to further populate the collection). Hit Next - the wizard intelligently suggests a name for the request collection. Hit Finish. The wizard launches a request collection that contains the individual Dossiers. You can then add the request collection as a favorite and open it everyday to get rich, contextual competitive intelligence.
The researchers may wish to review a particular dossier, and can do so by instructing the system to Show a Dossier on the CEO (e.g., named John Smith):
a. Using the wizard, launch an information-type request/agent for People with the keywords "John Smith".

b. Select the result and click "Save as Entity" - this saves the object as a "Person"
entity (because the semantic browser "knows" the original object is a person -as such, a "Person" entity makes sense in this context).
c. Using the Create Request Wizard, select the Dossier context template, and then select the "John Smith" entity as a filter. Hit Next - the wizard intelligently suggests a name for the request based on athe semantics of the request. The wizard also selects the right default predicates based on the semantics of the "John Smith" person entity. Hit Finish. The wizard compiles the query, sends the SQl'vIL to the KISes in the selected profile, and then displays the results (as sub-querieslagents) as follows:
~ All Bets by John Smith ~ Best Bets by John Smith ~ Breaking News by John Smith ~ Headlines by John Smith ~ Random Bets by John Smith ° Experts lilce John Smith (this returns Experts that have expertise on the same categories as those in which John Smith has expertise) ~ Newsmakers like John Smith (this returns Newsmakers that have recently "made news" in the same categories as those in which John Smith has recently "made news") ~ Interest Group like John Smith (this returns the people that have shown an interest in the same categories as those in which John Smith has shovJn interest -~n~ithin a time-windov~ (~-3 months in the preferred embodiment)) Conversations involving John Smith ~ Annotations by John Smith ~ Annotated Items by John Smith ~ Upcoming Events by John Smith ~ Popular Items by John Smith ~ Classics by John Smith The foregoing scenarios illustrate the operation of the system. The system itself is described in greater detail below.
B. SUBJECT MATTER FOR THE PRESENTLY PREFERRED EMBODIMENT OF
THE INFORMATION NERVOUS SYSTEM
Several improvements, enhancements and variations have been developed since the filing of my co-pending parent application and prior provisional applications referenced above. Some of these are improvements on, or only clarifications of, features previously included in the parent application, and some are new features of the system altogether. These are listed and described below. They are not arranged in order of importance, or in any particular order. While the preferred embodiment of the present invention would allow the user to use any or all of these features and improvements described below, alone or in combination, no single feature is necessary to the practice of the invention, nor any particular combination of features.
Also, in this application, reference is made to the same terms as are defined in my parent application Serial No. 10/179,651, and the Description throughout this application is intended to be read in conjunction with the definitions, terminology, nomenclature and Figures of my parent application except where the context of this application clearly indicates to the contrary.
1. Smart Selection Lens Overview The Smart Selection Lens is similar to the Smart Lens feature of the Information Nervous System information medium. In this case, the user can select text within the object and the lens will be applied using the selected text as the object (dynamically generating new "images" as the selection changes). This way, the user can "lens" over a configurable subset of the object metadata, as opposed to being constrained t~ "lens" over either the entire object or nothing at all.
This feature is similar to a selection cursor/verb overloaded with context.
For example, the user can select a piece of text in the Presenter and hit the "Paste as Lens" icon over the object in which the text appears. The Presenter will then pass the text to the client runtime component (e.g., an ActiveX object) with a method call like:
bstrSRML = GetSRMLForText( bstrText );
This call then returns a temporary SRML buffer that encapsulates the argument text. The Presenter will then call a method like:
bstrSQML = GetQueryForSmartLensOnObject( bstrSRMLObject );
This method gets the SQML from the clipboard, takes the argument SRML for the object, and dynamically creates new SQML that includes the resource in the SRML as a link in the 1s SQML (with the default predicate "relevant to"). The method then returns the new SQML. The Presenter then calls the method:
ProcessSemanticQuery( bstrSQML);
This method passes the generated lens SQML and then retrieves the number of items in the results and the SRML results, preferably asynchronously. For details on this call, see the specification "Information Nervous System Semantic Runtime OCX." The Presenter then displays a preview window (or the equivalent, based on the current skin) with something like:
[Lens Agent Title]
Found 23 items [PREVIEW OBJECT 1 [PREVIEW WINDOW CONTROLS ]
where the "Lens Agent Title" is the title of the agent on the clipboard. For details of the preview window (and the preview window controls), please refer to my parent application Serial No. 10/179,651.
In the preferxed embodiment, the preview window will:
~ Disappear after a timer expires (maybe SOOms) - on mouse move, the timer is preferably reset (this will avoid flashing the window when the user moves the mouse around the same area).
Fade out slowly (eventually).
The preferred embodiment also has the following features:
1. One selection range per object but multiple selections per results-set is the best option.
Otherwise, the system would result in a confusing user experience arid complex IJI to show lens icons per selection per object (as opposed to per object).
2. Outstanding lens query requests (which are regular SQML queries, albeit with SQML
dynamically generated consistent with the agent lens) should be cancelled when the Presenter no longer needs them (e.g. if the Presenter is navigating to a new page, or if we are requesting new lens info for an object). In any case, such cancellation is not critical from a performance (or bandwidth) standpoint because lens queries will likely only ask for a few objects at a time. Even if the queries are not cancelled, the Presenter can ignore the results. Regardless, because the Presenter also has to deal with stale results, dropping them on the floor the Presenter will have to do this anyway (whether or not lens queries are also cancelled). There will be a window of delay between when the Presenter issues a cancel request and when the cancellation actually is complete.
Because some results can trickle in during this time, they need to be discarded. Thus, the preferred embodiment has asynchronous cancellation implementations - the software component has been designed to always be prepared to ignore bad or stale results.
3. The Presenter preferably has both icons (indicating the current lens request state) and tool-tips: When the user hovers over or clicks on an object, the Presenter can put up a tool-tip with the words, "Requesting Lens Info" (or words to that effect). When the info comes back, hovering will show the "Found 23 Objects" tip and clicking will show the results. This interstitial tool tip can then be transitioned to the preview window if it is still up when the results arrive.
In addition, note that the smart selection lens, like the smart lens, can be applied to objects other thaw textual metadata. For instance, the Smart Selection Lens can be applied to images, video, a section of an audio stxeam, or other metadata. In these cases, the Presenter ~,~~uld return the appropriate SRI~1L consistent with the data type and the "selection region."
This region could be an area of an image, or video, a time span in an audio stream, etc. The rest of the smart lens functionality would apply as described above, with the appropriate SQML
being generated based on the SRML (which in turn is based on the schema for the data type under the lens).
2. Pasting Person Objects Overview The Information Nervous System (which, again, is one of our current shorthand names for certain aspects of our presently preferred embodiments) also supports the drag and drop or copy and paste of 'Person' objects (People, Users, Customers, etc.). There are at least two scenarios to illustrate the operation of the preferred embodiment in this case:

1. Pasting a Person object on a smart request representing a Knowledge community (or Agency) from whence the Person came. In this case, the server's semantic query processor merely resolves the SQML from the client using the Person as the argument. For instance, if the user pastes (or drags and drops) a person 'Joe' on top of a smart request 'Headlines on Reuters,' the client will create a new smart request using the additional argument. The Reuters Information Nervous System Web service will then resolve this request by returning all Headlines published or annotated by ''Joe.' In this case, the server will essentially apply the proper default predicate ('published or annotated by') - that makes sense for the scenario.
2. Pasting a Person object on a smart request representing a Knowledge community (or Agency) from whence the Person did not come. In this case, because the Person object is not in the semantic network of the destination Knowledge community (on its SMS), the server's semantic query processor would not be able to make sense of the Person argument. As such, the server must resolve the Person argument, in a different way, such as, for example, using the categories on which the person is an expert (in the preferred embodiment) or a newsmaker. For instance, talcing the above example, if the user pastes (or drags and drops) a person 'Joe' on top of a smart request 'Headlines on Reuters' and Joe is not a person on the Reuters knowledge community, the Reuters Web service (in the preferred embodiment) must return Headlines that are "relevant to Joe's expertise." This embodiment would then require that the client take a two-pass approach before sending the SQML to the destination Web service. First, it must aslc the Knowledge community that the person belongs to for "representative data (SRML)" that represents the person's expertise. The Web service resolves this request by:
a. Querying the Knowledge community (e.g., Reuters) on which the person object is pasted or dropped for that community's semantic domain information which comprises andlor represents that community's specifictaxonomy and ontology. Note that there could be several semantic domains.
b. Querying the Knowledge community from whence the person object came for that person object's semantic domain information.

c. If the semantic domains are identical or if there is at least one common semantic domain, the client queries the Knowledge community from whence the person came for the person's categories of expertise. The client then constructs SQML with these categories as arguments and passes this SQML to the Knowledge community on which the person was pasted or dropped.
If the semantic domains are not identical or there is not least one common semantic domain, the client queries the Knowledge community from whence the person came for several objects that belong to categories on which the person is an expert. In the preferred embodiment, the implementation should pick a high enough number of objects that accurately represent the categories of expertise (this number is preferably picked based on experimentation). The reason for picking objects in this case is that the destination Web service will not understand the categories of the Knowledge community from whence the person came and as such will not be able to map them to its own categories. Alternatively, a category mapper can be employed (via a centralized Web service on the Internet) that maps categories between different Knowledge Communities. In this case, the destination Knowledge community will always be passed categories as part of the SQML, even though it does not understand those categories - the Knowledge cornrnunity will then map these categories to internal categories using the category mapper Web service. The category mapper Web service will have methods for resolving categories as well as methods for publishing category mappings.
3. Saving and Sharing Smart Requests Overview Users of the liiformation Nervous System semantic browser (the Information Agent or Librarian) will also be able to save smart requests to disk, email them as an attachment, or share them via Instant Messenger (also as an attaclunent) or other means. The client application will expose methods to save a smart request as a sharable document. The client application will also expose methods to share a smart request document as an attachment in email or Instant Messenger.

A sharable smart request document is a binary document that encapsulates SQML
(via a secure stream in the binary format). It provides a safe, serialized representation of a semantic query that, among other features, can protect the integrity and help protect the intellectual property of the specification. For example, the query itself may embody trade secrets of the researcher's employer, which, if exposed, could enable a competitor to reverse engineer critical competitive information to the detriment of the company. The protection can be accomplished in several ways, including by strongly encrypting the XML version of the semantic query (the SQML) or via a strong one-way hash. The sharable document has an extension (.REQ) that represents the request. An extension handler on the client operating system is installed to represent this extension. When a document with the extension is opened, the extension handler is invoked to open the document. The extension handler opens the document by extracting the SQML from the secure stream, and then creating a smart request in the semantic namespace with the SQML. The handler then opens the smart request in the semantic namespace.
When a smart request in the semantic namespace is saved or if the user wants to send it as an email attachment, the client serializes the SQ1VIL representing the smart request in the binary .REQ format and saves it at the requested directory path or opens the email client with the .REQ
document as an attachment.
Figure 3 shows the binary document format that encapsulates the SQML buffer with the smart request and also illustrates how the extension handler opens the document. A similar model can also be employed for sharing results (via SRML). In this case, a binary document encapsulates the SRML, rather than the SQML as in the case above.
Figure 4A and A.B shows an illustration of two .REQ documents (titled 'Headlines on Reuters Related to My Research Report (Live)' and 'Headlines on Reuters (as of January 21 2003, 08 17AM)' on the fax right) with a registered association in the Windows shell. The first request document is 'live' and the second one is a snapshot at a particular time (they are both time-sensitive requests). Notice that the operating system has associated the semantic browser application (Nervana Librarian) with the document. When the document is opened, the semantic query gets opened in the application.
~ Saving and sharing entities - the same process applies as above except with a .ENT extension to represent an entity. When an entity document is invoked, the Nervana Librarian opens the entity SQML in the browser.
~ Extension Property Sheet - this will create a temporary smart request or entity (depending on the kind of document) in the semantic enviromnent and display the property sheet for a smart request or entity.
~ Extension Tool tips - this will display a helpful tool tip when the user hovers over a librarian document (a request, .REQ or an entity, .ENT).
4. Saving and Sharing Smart Snapshots Overview The Information Nervous System also supports the sharing of what the inventor calls "Smart Snapshots." A smart snapshot is a smart request frozen in time. This will enable a scenario where the user wants to share a smart request but not have it be "live." For instance, by default, if the user shares the smart request "Breaking News on Reuters related to this document"
with a colleague, the colleague will see the live results of the smart request (based on the '°current time"). Hovrever, if the user wants to share "[Current]
Breaking News on Reuters related to this document," a smart snapshot will be employed.
A smart snapshot is the same as a smart request (it is also represented by an SQML query document) except that the "attributes" section of the SQML document contains attributes marking it as a snapshot (the flag QUERYATTRIBUTES-SNAPSHOT). The creation date/time of the SQML document is also stored in the SQML (as before - the SQML schema contains a field for the creation date/time). When the user indicates that he/she wants to share the smart request, the user interface (the semantic browser, Information Agent, or Librarian) prompts him/her whether he/she wants to share the smart request (live) or a smart snapshot. If the user indicates s smart request, the process described above (in Part 3) is employed. If the user indicates a smart snapshot, the binary document is populated with the edited SQML (containing the snapshot attribute) and the remainder the process is followed as above.
When the recipient of the binary document receives it (by email, instant messaging, etc.), and opens it, the extension handler opens the document and adds an entry into the semantic namespace as a smart request (as described above). When the recipient opens the smart request, the client's semantic query processor will send the processed SQML to the server's XML web service (as previously described). The server's semantic query processor then processes the SQML and honors the snapshot attribute by invoking the semantic query relative to the SQML
creation date/time. As such, results will be relative to the original date/time, thereby honoring the intent of the sender.
5. Virtual Knowledge Communities Virtual Knowledge Communities (agencies) refer to a feature of the Information Nervous System that allows the publisher of a knowledge community to publish a group of servers to appear as though they were one server. For instance, Reuters could have per-industry Reuters Knowledge Communities (for pharmaceuticals, oil and gas, mmufacturing, financial services, etc.) but might also choose to expose one 6Reuters' knowledge community. To do this, Reuters will publish and announce the SQML for the virtual knowledge community (rather than the ZJRL
to the WSDL of the XML Web Service). The SQML will contain a blender (or collection) of the WSDLs of the actual Knowledge Communities. The semantic browser will then pick up the SQML and display an icon for the knowledge community (as though it were a single server).
Any action on the knowledge community will be propagated to each server in the SQML. If the user does not have access for the action, the Web service call will fail accordingly, else the action will be performed (no different from if the user had manually created a blender containing the Knowledge Communities).

6. Implementing Time-Sensitive Semantic Queries Semantic queries that are time-sensitive are preferably implemented in an intelligent fashion to account for the rate of knowledge generation at the knowledge community (agency) in question. For instance, 'Breaking News' on a server that receives 10 documents per second is not the same as 'Breaking News' on a server that receives 10 documents per month. As such, the 'server-side semantic query processor would preferably adjust its time-sensitive semantic query handling according to the rate at which information accumulates at the server.
To implement this, general rules of thumb could be used, for instance:
~ The most recent N objects where N is adjusted based on the number of new objects per minute.
~ All objects received in the last N minutes with a cap on the number of objects (i.e., min (cap, all objects received in the last N minutes)).
N can also be adjusted based on whether the query is a Headline or Breaking News. In the preferred embodiment, newsmaker queries is preferably implemented with the same time-sensitivity parameters as Headlines.
7. Text-To-Speech Skins Overview 'Text-to-speech is implemented at the object level and at the request level.
At the object level, the obj ect skin runs a script to take the SRML of the obj ect, interprets the SRML, and then passes select pieces of text (in the SRML fields) to a text-to-speech engine (e.g., using the Microsoft Windows Speech SDI) that generates voice output.
Figure 5 shows a diagram illustrating text-to-speech object skin. When executed, the pipeline shown in Figure 5 results in the following voice output:
1. Reading Email Message 2. Appropriate Delay 3. Message From Nosa Omoigui 4. Appropriate Delay 5. Message Sent to John Smith 6. Appropriate Delay 7. Message Copied To Joe Somebody 8. Appropriate Delay 9. Message Subject Is Web services are software building blocks used for distributed computing 10. Appropriate Delay 11. Message Summary is Web services 12. Appropriate Delay 13. [Optional] Message Body is Web services are software building blocks used for distributed computing This example assumes a voice skin template as follows:
1. Reading Email Message 2. Appropriate Delay 3. Message From <message author name>
4. Appropriate Delay 5. Message Sent to <message to: recipient name>
6. Appropriate Delay 7. Message Copied To <message cc: recipient name>
8. Appropriate Delay 9. Message Subject Is <message subject text>
10. Appropriate Delay 11. Message Summary is <message body summary>
12. Appropriate Delay 13. [Optional] Message Body is <message body>
Other templates can also be used to render voice that is easily understandable and which conveys the semantics of the object type being rendered. Like the example shown above (which is for email), the implementation should use appropriate text-to-speech templates for all information object types, in order to capture the semantics of the object type.
At the request level, the semantic browser's presentation engine (the Presenter) loads a skin that takes the SRML for all the current objects being rendered (based on the user-selected cursor position) and then invokes the text-to-speech object skin for each object. This essentially repeats the text-to-speech action for each XML obj ect being rendered, one after another.
Email Obj ect (SRML) Object Interpretation Engine (Object Skin) Text-to-Speech Engine From: Nosa Omoigui To: John Smith Cc: Joe Somebody Subject: Web services Summary: Web services are software building blocks used for distributed computing Body: Web services...

Voice Output Reading Email Message Delay Voice Output Message From Nosa Omoigui Delay Voice Output Message Sent To John Smith Delay Voice Output Message Copied To Joe Somebody Delay Message Subj ect is Web services are software building blocks used for distributed computing Voice Output Delay Voice Output Message Summary is Web services Delay Voice Output Message Summary is Web services Figure 6 shows an illustration of several email objects being presented in the semantic browser via a request skin.
From: Nosa Omoigui To: John Smith Cc: Joe Somebody Subject: Web services Summary: Web services are software building blocks used for distributed computing Body: Web services...
Email Obj ect 1 Object Skin (Object 1) Email Obj ect 2 Email Object 3 Email Obj ect N
8. Language Translation Skins Language translation skins are implemented similar to text-to-speech skins except that the transform is on the language axis. The XSLT skin (smart style) can invoke a software engine to automatically perform language translation in real-time and then generate XML that is encoded in Unicode (16 bits per character) in order to account for the universe of languages. The 2s XSLT transform that generates the final presentation output then will render the output using the proper character set given the contents of the translated XML.
Lasaguage agnostic sefnahtic queries Semantic queries can also be invoked in a language-agnostic fashion. This is implemented by having a translation layer (the SQML language translator) that translates the SQML that is generated by the semantic browser to a form that is suitable for interpretation by the KDS (or KBS) which in turn has a knowledge domain ontology seeded for one or more languages. The SQML language translator translates the objects referred to by the predicates (e.g., keywords, text, concepts, categories, etc.) and then sends that to the server-side seW antic query processor for interpretation. The results are then translated back to the original language by the language translation skin.
9. Categories as First Class Objects in the User Experience This refers to a feature by which categories of a knowledge community are exposed to the end user. The end user will be able to issue a query for a category as an information type -e.g., 'Web services.' The metadata will then be displayed in the semantic browser, as would be the case for any first-class information object type. Visualizations, d5mamic links, context palettes, etc. will also be available using the category object as a pivot.
This feature is useful in cases where the user wants to start with the category and then use that as a pivot for dynamic navigation, as opposed to starting off with a smart request (smart agent) that has the category as a parameter.
10. Categorized Annotations Categorized annotations follow from categories being first-class objects.
Users will be able to annotate a category directly - thereby simulating an email list that is mapped to a category. However, for cases where there are many categories (for instance, in pharmaceuticals), this is not recommended because information can belong to many categories and the user should not have to think about which category to annotate - the user should publish the annotation directly to the knowledge community (agency) where it will be automatically categorized or annotate an obj ect like a document or email message that is more contextual than a category.
11. Additional Context Templates 1. Experts - The Experts feature was indicated as a special agent in my parent application Serial No. 10/179,651. As should have also been understood from that application, the Experts feature can also operate in conjunction with the context templates section. Experts are a context template and as the name implies indicate people that have expertise on one or more subject matters or contexts (indicated by the PREDICATETYPEID EXPERTON
predicate).
2. Interest Group - this refers to a context template which as the name implies indicate people that have interest (but not necessarily expertise) on one or more subject matters or contexts (indicated by the PREDICATETYPEID INTERESTIN predicate). This context template returns People that have shown interest in any semantic category in the semantic networlc. A very real-world scenario will have Experts returning people that have answers and Interest Group returning results of people that have questions (or answers).
In the preferred embodiment, this is implemented by returning results of people who have authored information that in turn has been categorized in the semantic network, with the knowledge domains configured for the KIS. Essentially, this context template presents the user with dynamic, semantic communities of interest. It is a very powerful context template.
Currently, most organizations use email distribution lists (or the like) to indicate communities of interest.
However, these lists are hard to maintain and require that the administrator manually track (or guess) which people in the organization preferably belong to the list(s). With the Interest Group context template, however, the "lists" now become intelligent and semantic (akin to "smart distribution lists"). They are also contextual, a feature that manual email distribution lists lack.
Like with other context templates, the Interest Group context predicate in turn is interpreted by the server-side semantic query processor. This allows powerful queries like "Interest Group on XML" or "Interest Group on Bioinformatics." Similarly, this would allow queries (via drag, and drop and/or smart copy and paste) like "Interest Group on My Local Document" and "Interest Group on My Competitor (an entity)." The Interest Group context template also becomes a part of the Dossier (or Guide) context template (which displays all special agents for each context templates and loads them as sub-queries of the main agent/request).
In the preferred embodiment, the context template should have a time-limit for which it detects "areas of interest." An example of this would be three months. The logic here is that if the user has not authored any information (most typically email) that is semantically relevant to the SQML filter (if available) in three months, the user either has no interest in that category (or categories) or had an interest but doesn't any longer.
3. Annotations of My Items - this is a context template that is a variant of ,~,n_n_otations but is further filtered with items that were published by the calling user. This will allow the user to monitor feedback specifically on items that he/she posted or annotated.
12. Importing and Exporting User State The semantic browser will support the importation and exportation of user statee The user will be able to save his/her personal state to a doeument and export it to another machine or vice-versa. This state will include information (and metadata) on:
~ Default user state (e.g., computer sophistication level, default areas of interest, default job role, default smart styles, etc.) ~ Profiles ~ Entities (per profile) ~ Smart requests (per profile) ~ Local Requests (per profile) ~ Subscribed Knowledge Communities (per profile) The semantic browser will show UI (lilcely a wizard) that will allow the user to select which of the user state types to import or export. The UI will also ask the user whether to include identity/logon information. When the UI is involved, the semantic browser will serialize the user state into an XML document that has fields corresponding to the metadata of all the user state types. When the XML document is imported, the semantic browser will navigate the X1VIL
document nodes and add or set the user state types in the client environment corresponding to the nodes in the XML document.
13. Local Smart Requests Local smart requests would allow the user to browse local information using categories from an knowledge community (agency). In the case of categorized local requests, the semantic client crawls the local hard drives, email stores, etc. extracts the metadata (including summaries) and stores the metadata in a local version of the semantic metadata store (SMS). The client sends the AML metadata (per obj ect) to an knowledge community for categorization (via its XML Web Service). The knowledge community then responds with the category assignment metadata. The client then updates the local semantic network (via the local SMS) and responds to semantic queries just like the server would. Essentially, this feature can provide functionality equivalent to a local server without the need for one.
14. Integrated Navigation Integrated Navigation allows the user to dynamically navigate from within the Presenter (in the main results pane on the right) and have the navigation be integrated with the shell extension navigation on the left. Essentially, this merges both stacks. In the preferred embodiment, this is accomplished via event signaling. When the Presenter wants to dynamically navigate to a new request, it sets some state off the GUID that identifies the current browser view. The GUID maps to a key in the registry that also has a field called 'Navigation Event,' 'Next Namespace Object m' and 'Next Path.' The 'Navigation Event' field holds a DWORD
value that points to an event handle that gets created by the current browser view when it is loaded. When the Presenter Wants to navigate to a new request, it creates the request in the semantic environment and caches the returned ID of the request. It then dynamically gets the appropriate namespace path of the request (depending on the informationlcontext type of the request) and caches that too. It then sets the two fields ('Next Namespace Object m' and 'Next Path' with these two values). Next, it sets the 'Navigation Event' (in Windows, this is done by calling a Win32 API named 'SetEvent').
To catch the navigation event, the browser view starts a worker thread when it first starts.
This thread waits on the navigation event (and also simultaneously waits on a shutdown event that gets signaled when the browser view is being terminated - in Windows, it does this via a Wiii32 API named 'WaitForMultipleObjects'). If the navigation event is signaled, the 'Wait' API returns indicating that the navigation event was signaled. The worker thread then looks up the registry to retrieve the navigation state (the object id and the path). It then calls the shell browser to navigate to this object id and path (in Windows, this is done by retrieving a 'PIDL' and then calling IShellBrowser::BrowseTo off the shell view instance that implements IShellView).
15. Hints for Visited Results The Nervaala semantic browser empowers the user to dynamically navigate a lmowledge space at the speed of thought. The user could navigate along context, information or time axes.
However, as the user navigates, he/she might be presented with redundant information. For instance, the user can navigate from a local document to 'Freaking News' and then from one of the 'Freaking News' result obj acts to 'Headlines.' However, semantically, some of the Headlines might overlap with the breaking news (especially if not enough time has elapsed).
This is equivalent to browsing the Web and hitting the same pages over and over again from different 'angles.' The Nervana semantic browser handles this redundancy problem by having a local cache of recently presented results. The Presenter then indicates redundant results to the user by showing the results in a different color or some other UI mechanism. The local cache is aged (preferably after several hours or the measured time of a typical 'browsing experience'). Old entries are purged and the cache is eventually reset after enough time might have elapsed.

Alternately, at the users option, the redundant results can be discarded and not presented at all. Specifically, the semantic browser will also handle duplicate results by removing duplicates before rendering them in the Presenter - for instance if objects with the same metadata appear on different Knowledge Communities (agencies). The semantic browser will detect this by performing metadata comparisons. For unstructured data like documents, email, etc., the semantic browser will compare the summaries - if the summaries are identical the documents are very likely to be identical (albeit this is not absolutely guaranteed, especially for very long documents).
16. Knowledge Federation Client-Side Kyaowledge Federation Client-side Knowledge Federation which allows the user to federate knowledge communities and operate on results as though they came from one place (this federation feature was described in my parent Application Serial No. 10/179,651). In the preferred embodiment, such Client-side Knowledge Federation is accomplished by the semantic browser merging SRML results as they arrive from different (federated) KISes.
~'e~~~er-,Side Ka~owled~-e Federation Server-Side Knowledge Federation is technology that allows external knowledge to be federated within the confines of a lcnowledge community. For instance, many companies rely on external content providers like Reuters to provide them with information.
However, in the Information Nervous System, security and privacy issues arise - relating to annotations, personal publications, etc. Many enterprise customers will not want sensitive annotations to be stored on remote servers hosted and managed by external content providers.
To address this, external content providers will provide their content on a KIS metadata cache, which will be hosted and managed by the company. For instance, Reuters will provide their content to a customer like Intel but Intel will host and manage the KIS.
The Intel KIS
would crawl the Reuters KIS (thereby chaining KIS servers) or the Reuters DSA.
This way, sensitive Intel annotations can be published as 'Post-Its' using Reuters content as context while Intel will still maintain control over its sensitive data.
Federated Anyaotatiohs Federated annotations is a very powerful feature that allows the user to annotate an obj ect that comes from one agency/server (KIS) and annotate the object with comments (and/or attachment(s)) - like "Post-Its" on another server. For example, a server (call it Server A) might not support annotations (this is configurable by the administrator and might be the common case for Internet-based servers that don't have a domain of trust and verifiable identity). A user might get a document (or any other semantic result) from Server A but might want to annotate that object on one or more agencies (KISes) that do support annotations (more typically hitranet or Extranet-based agencies that do have a domain of trust and verifiable identity). In such a case, the annotation email message would include the URI of the object to be annotated (the email message and its attachments) would contain the annotation itsel f . When the server crawls its System Inbox and picks up the email annotation, it scans the annotation's encoded To or Subject field and extracts the URI for the object to be annotated. If the URI refers to a different server, the server then invokes an XML Web Service call (if it has access) to that server to get the SRI~1~L metadata for the object. The server then adds the SRI~L metadata to its Semantic Ieiletadata Store (S1VIS) and adds the appropriate semantic links from the email annotation to the SRML object. This is very powerful because it implies that users of the agency would then view the annotation and also be able to semantically navigate to the annotated object even though that object came from a different server.
If the destination server (for the annotation) does not have access to the server on which the object to be annotated resides, the destination server informs the client of this and the client then has to get the SRML from the server (on which the object resides) acid send the complete SRML back to the destination server (for the annotation). This embodiment essentially implies that the client must first "de-reference" the IJRI and send the SRML to the destination server, rather than having the destination server attempt to "de-reference" the URI
itself. This approach might also be superior for performance reasons as it spreads the CPU and I/O
load across its clients (since they have to do the downloading and "de-referencing" of the URI
to SRML).
Semantic Alerts for Federated Annotations I11 the same manner that semantic browser would poll each KIS in the currently viewed user profile for "Breaking News" relevant to each currently viewed object on a regular basis (e.g., every minute), the same will be performed for annotations. Essentially, this resembles polling whether each object that is currently displayed "was just annotated."
For annotations that are not federated (i.e., annotations that have strong semantic links to the objects they annotate), this is a straightforward SQML call back to the KIS from whence the annotated object came.
However, for federated annotations, the process is a bit more complicated because it is possible that a copy of object has been annotated on a different KIS even though the KIS from whence the object came doesn't support annotations or contain an annotation for the 'specific object.
In this case, for each object being displayed, the semantic browser would poll each KIS
in the selected profile and pass the URI of the object to "ask" the KIS
whether that object has been annotated on it. This way, semantic alerts will be generated even for federated annotations.

Annotation Hints This refers to a feature where the KIS returns a context attribute indicating that an object has been annotated. This can be cached when the KIS detects an annotation (typically from the System Inbox) and is updating the semantic network. This context attribute then becomes a performance optimizer because for those objects with the attribute set, the client wouldn't have to query the KIS again to .check if the object has been annotated. This amounts to caching the state of the object to avoid an extra (and unnecessary) roundtrip call to the KIS.
Attotlzer Pef~spective otz Ahttotations An interesting way to tlunk of the Simple and Semantic Annotations feature of the W formation Nervous System is that now every object/itexn/result in a user's knowledge universe will have its own contextual inbox. That way, if a user views the obj ect, the inbox that is associated with the object's context is always available for viewing. In other words, Category l~Tamitt~ a>z.d Iden.tifzcatiott (U~s) f~f~ p'edet~ated l~ttowZedge Coftamuftities This refers to how categories will be named on federated knowledge communities. For instance, a Reuters knowledge community (agency) deployed at Intel will be named Reuters aOIntel with categories named like 'Reuters@Intel/Information Technology/Verireless/80211'. In the preferred embodiment, every category will be qualified with at least the following properties:
~ Knowledge Domain ID - this is a globally unique identifier that uniquely identifies the knowledge domain from whence the category came ~ Name - this is the name of the category ~ Path - this is the full taxonomy path of the category The preferred embodiment, the categories knowledge domain id (and not the name) is preferably used in the category URI, because the category could be renamed as the knowledge domain evolves (but the identifier should remain the same). An example of a category URI in the preferred embodiment is:
nerv://c9554bce-aedf 4564-81f7-48432bf8e5a0?type=category&path= Information Technology/Wireless/80211 In this example, the knowledge domain id is c9554bce-aedf 4564-81f7-48432bf8e5a0, the URI type is "category" and the category path is "Information Technology/Wireless/80211".
17. Anonymous Annotations and Publications The semantic browser will also allow users to anonymously annotate and publish to an lmowledge community (agency). In this mode, the metadata is completely stored (with the user identity) but is flagged indicating that the publisher wishes to remain anonymous. This way, the Inference Engine can infer using the complete metadata but requests fox the publisher will not reveal his/her identity. Alternately, the administrator will also be able to configure the knowledge community (agency) such that the inference engine cannot infer using anonymous annotations or publications.
18. Offline Support in the Semantic Browser The semantic browser will also have offline support. The browser will have a cache for every remote call. The cache will contain entries to XML data. This could be ShML or could be any other data that gets returned from a call to the XML Web Service. Each call is given a iulique signature by the semantic browser and this signature is used to hash into the XML data.
For instance9 a serzlantic query is hashed by its S~I~Le ~ther rmnote calls are hashed using a combination of the method name, the argument names and types, aald the argument data.
Fox every call to the XML Web Service, the semantic runtime client will extract the signature of the call and then map this to an entry in the local cache. If the browser (or the system) is currently offline, the client will return the XML data in the cache (if it exists). If it does not exist, the client will return an error to the caller (likely the Presenter). If the browser is online, the client will retrieve the XML data from the XML Web Service and update the cache by overwriting the previous contents of the file entry with a file path indicated by the signature hash. This assumes that the remote call actually goes through - it might not even if the system/browser is online, due to network traffic and other conditions. In such a case, the cache does not get overwritten (it only gets overwritten when there is new data; it does not get cleared first).
19. Guaranteed Gross-Platform Support in the Semantic Browser Overview As discussed in my parent application (Serial No. 10/179,651), the Information Nervous System can be implemented in a cross-platform manner. Standard protocols are preferably employed where possible and the Web service layer should use interoperable Web service standards and avoid proprietary implementations. Essentially, the test is that the semantic browser does not have to "know" whether the Knowledge community (or agency) Web service it is talking to is running on a particular platform over another. For example, the semantic browser need not know whether the Web service it is talking to is running on Microsoft's .NETTM
platform or Sun's J2EE platform (to take 2 examples of proprietary application servers), a Linux or any other "open source" server. The IW owledge community Web service and the client-server protocol should employ Web service standards that are commonly supported by different Web service implementations like .NETTM and J2EETM.
In an ideal world, there will be a common set of standards that would be endorsed and properly implemented across Web service vendor implementations. however, this might not be the case in the real world, at least not yet. To handle a case where the semantic browser must handle unique fiznctionality in different Web service implementations, the Knowledge community schema is preferably extended to include a field that indicates the Web service platform implementation. For instance, a .NETTM implementation of the Knowledge community is preferably published with a field that indicates that the platform is .NETTM. The same applies to J2EETM. The semantic browser will then have access to this field when it retrieves the metadata for the Knowledge community (either directly via the WSDL URL to the Knowledge community, or by receiving announcements via multicast, the enterprise directory (e.g., LDAP), the Global Knowledge community Directory, etc.).

The semantic browser can then issue platform-specific calls depending on the platform that the Knowledge community is running on. This is not a recommended approach but if it is absolutely necessary to make platform-specific calls, this model is preferably employed in the preferred embodiment.
20. Knowledge Modeling Knowledge Modeling refers to the recommended way enterprises will deploy an Inf~rmation Nervous System. This involves deploying several KIS servers (per high-level knowledge domain) and one (or at most few) KDS (formerly KBS) servers that host the relevant ontology and taxonomy. KIS servers are preferably deployed per domain to strike a balance between being too narrow such that there is not enough knowledge sharing possibility of navigation and inference in the network and being too high that scalability (in storage and CPU
horsepower needed by the database and/or the inference engine) becomes a problem. ~f course, the specific point of balance will shift over time as the hardware and software technologies evolve, and the preferred embodiment does not depend on the particular balance struck. In addition, KIS servers are preferably deployed where access control becomes necessary at the server level (for higher-level security) as opposed to imposing access control at the group level with multiple groups sharing the same KIS. For instance, a large pharmaceutical company could have a knowledge community KIS for oncology for the entire company and another KIS for researchers working on cutting-edge R&D and applying for strategic patents.
These two KIS' might crawl the same sources of information but the latter KIS would be more secure because it would provide access only to users from the R&D group. Also, optionally, these researchers' publications and annotations will not be viewable on the corporate KIS.
Figure 7 illustrates an example of a possible knowledge architecture for a pharmaceuticals company. As shown in Figure 7, the KDS can serve several subsidiary KIS', as follows:
Client Knowledge Integration Server 1 (Oncology) Knowledge Integration Server 2 (Pharmacology) Knowledge Integration Server 3 (Biotechnology) Knowledge Integration Server 4 (Cardiology) Knowledge Domain Server (Pharmaceuticals) 21. KIS Housekeeping Rules The Knowledge Integration Server (KIS) will allow the admin to set up 'housekeeping' rules. to purge old or stale metadata. This will prevent the SMS on the KIS
from growing infinitely large. These rules could be as simple as purging any metadata older than a certain age (between 2-5 years depending on the company's policies for keeping old data) and which does not have any annotations and that is not marked as a favorite (or rated).
22. Client Component Integration & Interaction Workflow The client components of the system can be integrated in several different steps or sequences, as, can the workflow interaction or usage patterns. In the presently preferred embodiment, the workflow and component integration would be as follows:
1) Shell: User implicitly creates a SQML query (i.e. an agent) via UI
navigation or a wizard.
2) Shell: User opens an agent (via tree or folder view).
3) The query buffer is saved as a file, and a registry entry created is created for the agent.
a) IZegistr ~ entry contain : Agent l~Tarrie~ Creation date, Agent (I~equest)_ GUS, SQML path, Comments, ~Tamespace object type (agency, agent, blender, etc), and attributes 4) Shell: The request is handed off to the presenter:
a) A registry request GUID entry is created containing (namespace path that generated the request, and SQML file URL).
b) Browser is initialized and opened with command line http:/lPresenterPage.html#RequestGUlD http://presenterpage.html/. The Presenter loads default Chrome contained in the page.
c) Presenter page loads presenter binary behavior and Semantic Runtime OCX.
5) Presenter: Loads SQML and issues requests via the query manager.
a) Resolves request GUID to get SQML ale path.
b) Loads SQML file into buffer, creates resource handler requests, passes them to resource handlers, waits for and gathers results. Summarization of local resources happens here. All summarization follows one of two paths:
Summarize the doc indicated by this file path, or summarize this text (extracted from clipboard, Outlook, Exchange, etc.). Both paths produce a summary in the same form, suitable for inclusion in a request to the semantic server XML Web service.
c) Compiles SQML file into individual server request buffers, including any resource summary from above.
d) Initiates Server Requests by calling semantic runtime client Query Manager.
6) Query Manager: Monitors server requests and makes callback on data. It also signals an event on request completion or timeout. The callback is into the Presenter, which mean inter-process messaging to pass the XML.
7) Presenter: receives data and loads appropriate skin:
a) Receives SRML data in buffer; this will happen incrementally.
b) Determines if there is a preferred skin (smart style) associated with this agent, otherwise chooses default skin.
c) Transforms SRML into preferred skin format via XSLT. This is multistage, for the tree of results (root is list, then objects, then Deep/Lens/BN info) as results come in.
d) Display results in target DIV in page. The target is an argument to the behavior itself and is defined by the root page.
8) Presenter: Calls Semantic Runtime to fill context panels (per context template), deep info, smart copy and paste, and other semantic commands. The Presenter also loads the smart style, which then loads semantic images, motion, etc. consistent with the semantics of the request.
Figure 8 illustrates the presently pr eferred client component integration and interaction workflow described above.
23. Categories Dialog Box User Interface Speeifieation a. ~ver~rie~
The Categories Dialog Box allows the user to select one or more categories from a category folder (or taxonomy) belonging to a knowledge domain. While more or fewer can be deployed in certain situations, in the preferred embodiment, the dialog box has all of the following user interface controls:
1. Profile - this allows the user to select a profile with which to filter the category folders (or taxonomies) based on configured areas of interest. For instance, if a profile has areas of interest set to "Health and Medicine," selecting that profile will display only those category folders that belong to the "Health and Medicine" area of interest (for instance, Pharmaceuticals, Healthcare, and Genes). This control allows the user to focus on the taxonomies that are relevant to his/her knowledge domain, without having to see taxonomies from other domains.

2. Area of Interest - this allows the user to select a specific area of interest. By default, this combo box is set to "My Areas of Interest" and the profile combo box is set to "All Profiles." This way, the dialog box will display category folders for all areas of interest for all profiles. However, by using the "Area of Interest" combo box, the user can directly specify an area of interest with which to filter the category folders, regardless of the areas of interest in hislher profile(s).
3. Publisher Domain Zone/Name - this allows the user to select the domain zone and name of the taxonomy publisher. This is advantageous to distinguish publishers that might have name collisions. In the preferred embodiment, the Publisher Domain Name uses the DNS
naming scheme (for instance, IEEE.org, Reuters.com). The domain zone allows the user to select the scope of the domain name. In the preferred embodiment, the options are W ternet, Intranet, and Extranet. The zone selection further distinguishes the published category folder (or taxonomy). A fairly common case would be where a department in a large enterprise has its own internal taxonomy. In this case, the department will be assigned the Intranet domain zone and will have its own domain name - for instance, Intranet\Marketing or Intranet\Sales.
4. Category Folder - this allows the user to select a category folder or taxonomy.
When this selection is made, the categories for the selected category folder are displayed in the categories tree view.
5. Search categories - this allows the user to enter one or more keywords with which to filter the currently displayed categories. For instance, a Pharmaceuticals researcher could select the Pharmaceuticals taxonomy but then enter the keyword "anatomy" to display only the entries in the taxonomy that contain the keyword "anatomy."
6. "Remember" checlc box - this allows the user to specify whether the dialog box should "remember" the last search when it exits. This is very helpful in cases where the user might want to perform many similar category-based searcheslrequests from the same category folder and with the same keyword filter(s).

7. Search Options - these controls allow the user to specify how the dialog box should interpret the keywords. The options allow the user to select whether the keywords should apply to the entire hierarchy of each entry in the taxonomy tree, or whether the keywords should apply to only the [end] names of the entries. For instance, the taxonomy entry "Anatomy\Cells\Chromaffin Cells" will be included in a hierarchy filter because the hierarchy includes the word "Anatomy." However, it will be excluded from a names filter because the end-name ("Chromaffm Cells") does not include the word "Anatomy."
Also, the search options allow the user to select whether the dialog box should check for all keywords, for any keyword, or for the exact phrase.
8. Categories Tree View - the tree view displays the taxonomy hierarchy and allows the user to select one or more items to add to the Create Request Wizard or to open as a new Dossier (Guide) request/agent. The user interface breaks the category hierarchy into "category pages" - for performance reasons. The ITI allows the user to navigate the pages via buttons and a slide control. There is also a "Deselect All" button that deselects all the currently selected taxonomy items.
9. Explore Sutton - this is the main invocation button of the dialog box. When the dialog box is launched from the Create Request Wizard, this button is renamed to "bAdd" and adds the selected items to the wizard "filters" property page. When the dialog box is launched directly from the application, the button is titled "Explore" and when cliclced launches a Dossier request on the selected categories. If the user has multiple profiles or if multiple taxonomy categories are selected, the dialog box launches another dialog box, the "Explore Categories Options" dialog box that prompts the user to select the profile with which to launch the Dossier and/or the operator to use in applying the categories as filters to the Dossier (AND or OR).
The features described above are illustrated in Figures 9 - 11, which show three different views of the Explore Categories dialog box.

24. Client-Assisted Server Data Consistency Checking As the server (KIS) crawls knowledge sources, there will be times when the server's metadata cache is out of sync with the sources themselves. For instance, a web crawler on the KIS that periodically crawls the Web might add entries into the semantic metadata store (SMS) that become out of date. In this case, the client would get a 404 error when it tries to invoke the source URI. For data source adapters (DSAs) that have monitoring capabilities (for instance, for f le-shares that can be monitored for changes), this wouldn't be much of an issue because the KIS is likely to be in sync with the knowledge source(s). However, for sources such as Web sites that don't have monitoringlchange-notification services, this may present an issue of concern.
My parent application (Serial No. 10/179,651) described how the KIS can use a consistency checker (CC) to periodically purge stale entries from the SMS.
However, in some situations dais approach might impair performance because the CC would have to periodically scan the entire SMS and confimn whether the indexed objects still exist. An alternative embodiment of this feature of the invention is to have the client (the semantic browser) notify the server if it gets a 404 error. To do this, the semantic browser would have to track when it gets a 404 error for each result that the user 'fiopen~e" For ~l~leb do~a.~znent~~
the client can poll f~r the HTTP headers when it displays the results, even before the user opens the results. In this case, if the source web server reports a 404 error (object not found), the client should report this to the KIS.
When the KIS gets a "404 report" from the client, it then intelligently decides whether this means the object is no longer available. The KIS cannot arbitrarily delete the object because it is possible that the 404 error was due to an intermittent Web server failure (for instance, the directory on the Web server could have been temporarily disabled). The KIS
should itself then attempt to asynchronously download the object (or at the very least, the HTTP
headers in the case of a Web object) several times (e.g., 5 times). If each attempt fails, the KIS can then conclude that the object is no longer available and remove it from the SMS. If another client reports the 404 error for the same object while the KIS is processing the download, the KIS
should ignore that report (since it is redundant).
This alternate technique could be roughly characterized as lazy consistency checking. In some situations, it may be advantageous and preferred.
25. Client-Side Duplicate Detection The server (KIS) performs duplicate detection by checking the source URIs before adding new objects into the semantic metadata store (SMS). However, fox performance reasons, it is sometimes advantageous if the server does not perform strict duplicate-detection. In such cases, duplicate detection is best performed at the client. Furthermore, because the client federates results from several KISes, it is possible for the client to get duplicates from different KISes. As such, it is advantageous if the client also performs duplicate detection.
In the preferred embodiment, the client removes objects that are definitely duplicates and flags objects that are likely duplicates. Definite duplicates are objects that have the same URI, last modified time stamp, summaryfconcepts, and size. Likely duplicates are objects that have the same summarylconcepts, but have different URIs, last modifi ed times, or sizes. For obj acts for which summary extraction is difficult, it is recommended that the title also be used to check for lil~ely duplicates (i.e., objects that have the same summary but different titles are not considered likely duplicates because the summary might not be a reliable indicator of the contents of the object). Also, if summarylconcept extraction is difficult (in order to detect semantic overlap/redundancy), the semantic browser can limit the file-size check to plus or minus N % (e.g., 5%) - for instance, an object with the same summary/concepts and different URIs, last-modified times, and sizes might be disqualified as a likely duplicate if the file-size is within 5% of the file-size of the object it is being compared to for redundancy checking.
26. Client-Side Virtual Results Cursor The client (semantic browser) also provides the user with a seamless user experience when there are multiple knowledge communities (agencies) subscribed to a user profile. The semantic browser preferably presents the results as though they came from one source. Similarly, the browser preferably presents the user with one navigation cursor - as the user scrolls, the semantic browser re-queries the KISes to get more results. In the preferred embodiment, the semantic browser keeps a results cache big enough to prevent frequent re-querying - for instance, the cache can be initialized to handle enough results for between 5-10 scrolls (pages).
The cache size are preferably capped based on memory considerations. As the cursor is advanced (or retreated), the browser checks if the current page generates a cache hit or miss. If it generates a cache hit, the browser presents the results from the cache, else if re-queries the KISes for additional results which it then adds to the cache.
The cache can be implemented to grow indefinitely or to be a sliding window.
The former option has the advantage of simplicity of implementation with the disadvantage of potentially high memory consumption. The latter option, which is the preferred embodiment, has the advantage of lower memory consumption and higher cache consistency but with the cost of a more complex implementation. With the sliding window, the semantic browser will purge results from pages that do not fall within the window (e.g., the last N -e.g., 5-10 - pages as opposed to all pages as with the other embodiment).
2~. ~g~-~t~aal ~inglc ~gga~-~n The client (semantic browser) also provides the user with a seamless user experience when authenticating the user to his/her subscribed knowledge communities (agencies). It does this via what the inventor calls "virtual single sign-on." This model involves the semantic browser authenticating the user to knowledge communities without the user having to enter his/her username and password per knowledge cormnunity. Typically, the user will have a few usernames and passwords but might have many knowledge communities of which he/she is a member (especially within a company based on departmental or group access, and on Internet-based knowledge communities). As such, the ratio of the number of knowledge communities to the number of authentication credentials (per user) is likely to be very high.

With virtual single sign-on, the user specifies his/her logon credentials to the semantic browser in a server (knowledge community)-independent fashion. The semantic browser stores the credentials in a Credential Cache Table (CCT). The CCT has columns as illustrated below:
Account Name User Name Password Knowledge Community Entry List ~ Account Name - this is a friendly name for the account ~ User Name - this is the logon user name (e.g., an ernail address) ~ Password - this is the password, stored encrypted with a secure private key ~ Knowledge Community Entry List (KCEL) - this is a list of knowledge communities that authenticate the user using the credentials for this account When the user first attempts to subscribe to a knowledge community (or access the knowledge community in some other way - for instance, to get the properties of the community), the semantic browser prompts the user for his/her password and then tries to logon to the server using the supplied credentials. If a logon is successful, the semantic browser creates a new CCT
entry (CCTE) with the supplied credentials and adds the KC to the Knowledge Community Entry List (KCEL) for the new CCT entry.
For each subsequent subscription attempt, the semantic browser checks the CCT
to see if the KC the user is about to subscribe to is in the KCEL for any CCTE. If it is, the semantic browser retrieves the credentials for the CCTE and logs the user on with those credentials. This way, the user does not have to red~.andantly enter his/her logon credentials.
Note that the semantic browser also supports pass-through authentication when the operating system is already logged on to a domain. For instance, if a Windows machine is already logged on to an NT (or Active Directory) domain, the client-side Web service proxy also includes the default credentials to attempt to logon to a KC. In the preferred embodiment, the additional credentials supplied by the user are preferably passed via SOAP
security headers (via Web Services Security (WS-Security) or a similar scheme). For details of WS-Security and passing authentication-information in SOAP headers, see http://www.oasis-open.org/committees/download.php/3281/WSS-SOAPMessageSecurity-17-082703-merged.pdf The semantic browser exposes a property to allow the user to indicate whether the credentials for a CCTE are preferably purged when the KCEL for the CCTE is empty or whether the credentials should be saved. In the preferred embodiment, the credentials are preferably saved by default unless the user indicates otherwise. If the user wants the credentials purged, the semantic browser should remove a KC from a CCTE in which it exists when that KC is no longer subscribed to any profile in the browser. If after removing the KC from the CCTE's KCEL, the CCTE becomes empty, the CCTE is preferably deleted from the CCT.
The virtual single sign-on feature, like many of the features in this application, could be used in applications other than with my Information Nervous System or the Virtual Librarian.
For example, it could be adapted for use by any computer user who must log into more than one domain.
28. Namespace Object Action Matrix The table below shows the actions that the semantic browser invokes when namespace objects are copied and pasted onto other namespace objects.
Destination Portfolio' Knowledge ~

(EntityObject Default DossierCommunityApplication z -Source EntityCollection)(Result)ProfileProfileRequest(Guide)(Agency)(Root b - Tcon) Entity ObjectCopy ObjectCopy Copy Query DossierDossierN/A
(Open Lens Lens Query Query as bookmark (Dossier) (Dossier) (from in default KC) profile in alternative embodiment) PortfolioObjectCopy ObjectCopy Copy Query DossierDossierN/A
(Open (EntityLens (contents)Lens Query Query as bookmark Collection)(Dossier) (Dossier) (from in default KC) profile in alternative embodiment) Object ObjectObject ObjectCopy Copy Query DossierDossier(Open as (Result)Lens Lens Lens (Bookmark)(Bookmark) Query Query bookmark in (Dossier)(Dossier)(Dossier) (from default KC) rofile ProfileN/A N/A N/A N/A N/A NlA NlA NlA NlA

DefaultN/A N/A NlA N/A N/A N/A N/A N/A N/A

Profile RequestSmart Smart Smart Copy Copy Agent DossierDossierCopy Lens (to Lens Lens Lens Agent Agent default Lens Lens (from profile) KC) DossierDossierDossierDossierCopy Copy DossierDossierDossierCopy (to (Guide)Smart Smart Smart Agent Agent Agent default Lens Lens Lens Lens Lens Lens (from profile) KC) KnowledgeDossierDossierDossierCopy Copy DossierDossierDossierCopy CommunitySmart Smart Smart (subscribe)(subscribe)A ent Agent A ent (subscribe) Lens Lens Destination Portfolio Knowledge -> (EntityObject Default Dossier;CommunityApplication , a .

Source Entity, (Result)Profile= Profile,Request> -(Guide)(Agency)' (Root i Colleotion) . lcon),v (Agency)Lens (from Lens Lens Lens (from to default KC) (from ~ ~ (from ~ (from~ (from~ source~ profile KC) KC) KC) ~

KC) KC) 29. Dynamic End-to-End Ontology/Taxonomy Updating and Synchronization The Information Nervous SystemTM will support dynamic updates of ontologies and taxonomies. Knowledge domain plug-ins that are published by Nervana (or that are provided to Nervana by third-party ontology publishers) will be hosted on a central Web service (an ontology depot) on the Nervana Web domain (Nervana.com). Each KDS will then periodically poll the central Web service via a Web service call (for each of its knowledge domain plug-ins, referenced by the URI or a globally unique identifier of the plug-in) and will "ask" the Web service if the plug-in has been updated. The Web service will use the last-modified timestamp of the ontology file to determine whether the plug-in has been updated. If the plug-in has been updated, the Web service will return the new ontology file to the calling KDS.
The KDS then replaces its ontology file.
If the KDS is running during the update, it will ordinarily temporarily stop the service before replacing the file, unless it supports file-change notifications and reloads the ontology (which is the recommended implementation).
Each KIS also has to poll each I~I~S it is connected to in order to "ask" the KISS if its ontology has changed. In the preferred embodiment, the KIS should poll the KDS
and not the central Web service in case the KDS has a different version of the ontology.
The KDS also uses the last modified time stamp of the knowledge domain plug-in (the ontology) to determine if the ontology has changed. It then indicates this to the KIS. If the ontology has changed, the KIS
needs to update the semantic network accordingly. In the preferred embodiment, it does this by removing semantic links that refer to categories that axe not in the new version of the ontology and addinglmodifying semantic links based on the new version of the ontology.
In an alternative embodiment, it purges the semantic network and re-indexes it.
so The client then polls each KIS it is subscribed to in order to determine if the taxonomies it is subscribed to (directly via the central Web service or via the KISes) have changed. The KIS
exposes a method via the XML Web service via which the client determines if the taxonomy has changed (via the last modified time stamp of the taxonomy/ontology plug-in file). If the taxonomy has changed, the client needs to update the Categories Dialog user interface (and other UI-based taxonomy dependents) to show the new taxonomy.
For taxonomies that are centrally published (e.g., via Nervana), the client should poll the central Web service to update the taxonomies.
With this model, the client, ISIS, KDS, and central taxonomy/ontology depot will be kept synchronized.
30. Invoking Dossier (Guide) Queries Dossier Seynantic Query Pf~ocessing Dossier (guide) queries are preferably invoked by the client-side semantic query processor by parsing the SQML of the requestlagent and replacing the Dossier context predicate with each special agent (context template) context predicate - e.g., All Bets, Best Bets, Breaking News, I~eadlines9 Random Bets, Newsmakers9 etc. Each query (per context template) is then invoked via the query processor - just like an individual query. This way, the user operates at the level of the Dossier but the semantic browser maps the dossier to individual queries behind the scenes.
For example, the SQML for "Dossier on Category C" is parsed and new SQML
queries are generated as follows:
~ All Bets on Category C
~ Best Bets on Category C
~ Breaking News on Category C
~ Headlines on Category C
~ Random Bets on Category C
~ Newsmakers on Category C
~ Etc.

The client-side semantic query processor retains every other predicate except the context predicate. This way, the filters remain consistent as illustrated by the example above.
Dossier Smart Lens Like other requests/agents in the Information Nervous SystemTM, dossiers (guides) can be used as a Smart Lens (just like how they can be targets for drag and drop, smart copy and paste, etc.). In this case, the smart lens displays a "Dossier Preview Window" with sections/tabs/frames for each context template (special agent). Sample screenshots of the Dossier showing the UI of the Dossier Smart Lens are included in Figures 12 and 13.
Dossier Screenshots 31. Knowledge Community (Agency) Semantics The following describe the semantics of a knowledge community (agency) within the context of the semantic namespace/environment in the semantic browses:
1. Selecting a knowledge community - this opens a dossier request from that KC.
Essentially, the Dossier becomes the equivalent of the KC's "home page."
2. Drag and drop (document, text, entity, keywords, etc.) to a KC - this opens a Dossier request/agent on the object (using the default predicate) from the KC
3. Copy KC to the clipboard - this selects KC as the Smart Lens. When the user hovers over a result or entity, the semantic browses displays the Smart Lens by showing the KC
name and the KC's profile name under the cursor and then opens a Dossier from the KC on the obj ect underneath the lens in the lens preview pane 4. Subscribing to a KC - when a KC is subscribed for the first time, the semantic browses adds the KC's email address to the local email contacts (e.g., in Microsoft Outlook or Outlook Express). This makes it easy for the user to publish knowledge to the KC by sending it email (via the integrated contacts list). Similarly, when the KC is unsubscribed from all profiles, the semantic browses prompts the user whether it should remove the KC from the local email contacts list.

32. Dynamic Ontology and Taxonomy Mapping One of the challenges of using taxonomies and ontologies is how to map the semantics of one taxonomy/ontology onto another. The Information Nervous SystemTM
accomplishes this by the following algorithm:
Each KDS will be responsible for ontology mapping (via an Ontology Mapper (OM)) and will periodically update the central Web service (the ontology depot) with an Ontology Mapping Table (OMT). The updates axe bi-directional: the KDS will periodically update its ontologies and taxonomies from the central Web service and send updates of the OMT to the central Web service. Each OMT will be different but the central ontology depot will consolidate all OMTs into a Master OMT. The ontology mapper will create a consistent user experience because the user wouldn't have to select all items in the umbrella taxonomy that are relevant but overlapping.
The semantic browser will automatically handle this. The ISIS wouldn't have any concept of the mapper but will get mapped results from the I~I~S which it will then use to update the semantic network.
The KDS and ISIS administrators would still be responsible for selecting the right KDS
ontology plug-ins, however - based on the quality of each ontology/taxonomy (the ontology mapping doesn't improve ontologies~ it merely maps them).
33, semantic Alerts Optimizations Semantic Alerts in the semantic browser can be optimized by employing the following rule (in order):
For a given filter (e.g., result, document, text, keywords, entity):
' 1. Check for Headlines first.
2. If there are Headlines, check for Breaking News and Newsmakers.
This is because in the preferred embodiment, Headlines are implemented similar to Breaking News except with a larger time window. As a consequence, if there are no Headlines (in the preferred embodiment), there is no Breaking News. Also, in the preferred embodiment, Newsmakers are implemented by returning the authors of Headlines. As such, if there are no Headlines, there are no Newsmakers.
34. Semantic "News" Images Both Corbis (http://www.corbis.com) and Getty Images (http://www.gettyimages.com) have "News" images that are constantly kept fresh. The Information Nervous SystemTM can use these kinds of images for semantic images that are not only context-sensitive but also "fresh."
This can be advantageous in terms of keeping the user interface interesting and "new." For instance, "Breaking News on SARS" can show not only pharmaceutical images but images showing doctors responding to recent SARS outbreaks, etc.
35. Dynamically Choosing Semantic Images Semantic images can be dynamically and intelligently selected using the following rules:
1. If the currently displayed namespace object is a request, parse the SQMI, of the object for categories. If there are categories, send the categories to the central Web service (that hosts the semantic image cache) to get images that are relevant to the categories. Also, send the request type (e.g., knowledge types like All Bets and Headlines, or information types like Presentations) to the central Web service to return irrlages consistent vrith the request type:
2. If the namespace object is not a request, send the areas of interest for the current profile (if available) to the central Web service. The Web service then returns semantic images consistent with the profile's areas of interest. If the profile does not have configured areas of interest, send the areas of interest for the application (the semantic browser). If the application does not have configured areas of interest, send an empty string to the central Web service - in this case, the central Web service returns generic images (e.g., branded images).
36. Dynamic Knowledge Community (Agency) Contacts Membership Knowledge communities (agencies) have members (users that have read, write, or read-write access to the community) and contacts. Contacts are users that are relevant to the community but are not necessarily members. For example, a departmental knowledge community (KC) in a large enterprise would likely have the members of the department as members of the KC but would likely have all the employees of the enterprise as contacts.
Contacts are advantageous because they allow members of the KC to navigate users that are semantically relevant to the KC but might not be members. The KC might semantically index sent by contacts - the index in this case would include the contacts even though the contacts are not members of the KC.
Another way to think of this is that communities of knowledge in the real world tend to have core members and peripheral members. Core members are users that are very active in the community while peripheral members include "other" users such as knowledge hobbyists, occasional contributors, potential recruits, and even members of other relevant communities.
With dynamic KC contacts membership in the Information Nervous SystemTM, the KIS
will add users to its Contacts table in the semantic metadata store (SMS) and to the semantic network "when and as it sees them" (in other words, as it indexes email messages that have new users that are not members). This allows the community to dynamically expand its contacts, but in a way that distinguishes between Members and mere Contacts, and "understands" the importance of the distinction semantically when operating the system (e.g., executing searches and the like).
37. Integrated Full-Text Keyword and Phrase Indexing The KIS also indexes concepts (key phrases) and keywords as first-class members of the semantic network. This can be done in a domain-independent fashion as follows:
For each new object (e.g., documents) to be added to the semantic network:
1. Extract concepts (key phrases) from the body of the object.
2. For each concept, add the concept to the semantic network with the object type id OBJECTTYPEID_CONCEPT. Add a semantic link with the predicate PREDICATETYPEID_CONTAINSCONCEPT to the "Semantic Links" table with the new obj ect as subj ect and the new concept obj ect as the subj ect.
3. For the current concept, extract the keywords from the concept key phrase and add each keyword to the semantic network with the object type id OBJECTTYPEID KEYWORD. Also, add a semantic link with the predicate PREDICATETYPEID_CONTAINSKEYWORD to the "Semantic Links" table with the new obj ect as subj ect and the new keyword obj ect as the subj ect.
Repeat the steps above for the title of the object and other meta-tags as appropriate for the schema of the obj ect.
While some embodiments do not require integrated full-text indexing, it is included in the presently preferred embodiment because it provides several useful advantages:
1. It allows a consistent model for implementing semantic filters (in SQML).
The user can add categories, documents, entities, and keywords as filters and the filters are applied consistently to the semantic network (as sub-queries).
2. In particular, it supports the semantic query processing of entities.
Entities can be defined with categories and can be further narrowed with keywords (to disambiguate the keywords in the case where the keywords could mean different things in different contexts).
W tegrated full-text indexing allows the ISIS semantic query processor (SQP) to interpret entities seamlessly - by applying the necessary sub-queries with categories and keywords/concepts to the semantic network.
3. In general, integrated full-text indexing results in a seamless and consistent data and query model.
~f~. ~earaaantic '~IV~arlg ~bje~t a~ ll~cad99 In some cases, the ISIS might not have the resources to store semantic links between People and objects on a per-object basis. In addition, semantic-based redundancy is not the same as per-object redundancy - as in email. To take an example, email clients allow users to select an email message as read or unread - this is typically implemented as a flag stored on the mail server with the email message. However, because email is not a semantic system, a semantically similar or identical message on the server would not be flagged as such - the user has to flag each message separately regardless of semantic redundancy.
In the lilformation Nervous SystemTM, the user is able to flag an object as read not unlike in email. However, in this case, the semantic browser extracts the concepts from the object and informs all the KISes in the request profile that the "concepts" have been read. The KIS then dynamically maps the concepts to categories via the KDSes it is configured with and adds a flag to the objects belonging to those categories (in the preferred embodiment) and/or adds a flag to the semantic network with a semantic link with the predicate PREDICATETYPEID ~VIEWEDCATEGORY between the categories corresponding to the concepts and all the objects that are linked to the categories. In the preferred embodiment, the KIS should only flag those categories over a link-strength threshold (for the source concepts).
This ensures that only those objects (in the preferred embodiment) and/or categories that are semantically close to the original object will be flagged.
When the semantic browses flags the object via the KISes, the I~ISes should retunl a flag indicating whether the network was updated (it is possible that no changes would be made in the event that the object does not have any "strong" categories or if there are no other objects that share the same "strong" categories). If at least one ISIS in the request profile indicates that the network was updated, the semantic browses should refresh the request/agent.
The semantic browses can expose a property to allow the user to indicate whether he/she wants the I~ISes to return only unread objects or all objects (read or unread), in which case the browses should display unread objects differently (like how email clients display unread messages in a bold font). The presentation layer in the semantic browses should then display the read and unread obj ects with an appropriate font and/or color to provide a clear visual distinction.
39. Mufti-Select Object Lens Mufti-select object lens is an alternative implementation of the object lens that was described in my parent application. In that embodiment, the object lens was invoked via smart copy and paste - pasting an object over another object would invoke the object lens with the appropriate default predicate. This has the benefit of allowing the user to copy objects across instances of the semantic browses, across profiles, and from other environments (like the file-system, word processors, email clients, etc.).

In the currently preferred embodiment, the object lens is a Dossier Lens (the context predicate is a Dossier, the filters are the source and target objects, and the profile is the profile in which the source object was displayed).
Mufti-selection can also be used instead of copy and paste to invoke an object lens. The semantic browser will allow the user to select multiple objects (results). The user can then hit a button (or alternative user-interface object) to invoke the object lens on the selected objects. In this case, a Dossier Lens will be displayed (in a preview pane) with a Dossier context predicate, with the filters.as the selected objects, and the current profile as the request profile.
40. Ontology-Based Filtering and Spam Management The KIS (in the preferred embodiment) would only add objects to the Semantic Metadata Store (SMS) if those objects belong to at least one category from at least one of the knowledge domains the KIS is configured with (via one or more KDSes). This essentially means the KIS
will not index objects it "does not understand." The exception to this is that the KIS will index all objects from its System Inbox - because this contains at-times personal community-specific publications and annotations that might be relevant but not always semantically relevant.
t~ side-effect of this ontology-based filtering model is span managmnent -ontology-based indexing would be effective in preventing seam from being indexed and stored. If users use the semantic browser to access email, as opposed to their inboxes, only email that has been semantically filtered will get through.
41. Results Refinement The results of a request/agent can be further refined via additional filters and predicates.
For example, the requestlagent Headlines on Bioinformatics could be further refined with keywords specific to certain areas of Bioinformatics. This way, the end-user can further narrow the result set using the request/agent as a base. In addition, for time-sensitive requests, the user can specify a time-window to override the default time-window. For example, the default Breaking News time-request could be set to 3 hours. The user should be able to override this for 5s a specific request/agent (in addition to changing the defaults on a per-profile or application-wide basis) with an appropriate UI mechanism (e.g., a slider control that ranges from 1 hour to 24 hours). The same applies to Headlines and Newsmakers (e.g., a slider control that ranges from 1 day to 1 week).
When the user specifies a filter-oven-ide, the semantic browser invokes the XML Web Service call for each of the KISes in the request profile and passes the override arguments as part of the call. If override arguments are present, the Web service uses those values instead of the default filter values. The same applies to additional filters (e.g., keywords) - these will be passed as additional arguments to the Web service and the Web service will apply additional sub-queries appropriately to further filter the query that is specified in the agent/request SQML
(in other words, the SQML is passed as always, but in addition, the filter overrides and additional filters are also passed).
A good case for filter-overrides will be for Best Bets. The default semantic relevance strength for Best Bets could be set to 90% (in the preferred embodiment).
However, for a given request/agent, the user might want to see "bets" across a semantic relevance range. Exposing a relevance UI control (e.g., a slider control that ranges from 0% to 100%) will allow this. This essentially allows the user to change the Best Bets on the fly from "All Bets"
(0%) all the v~ay to "Perfect Bets" (100%).
A hybrid model should also be employed for embodiments of context template (special agent) implementations that involve multiple axes of filtering. For instance, Breaking News could also impose a relevance filter of 25% and Headlines and Newsmakers could impose a relevance filter of 50% (Breaking News has a lower relevance threshold because it has a higher time-sensitivity threshold; as such, the relevance threshold can be relaxed).
In this case, the semantic browser should expose UI controls to allow the user to refine the special agents across both axes (a slider control for time-sensitivity and another slider control for relevance).
With dossiers, the semantic browser can display UI controls for each special agent displayed in the Dossier - the main Dossier pane can show all the UI controls (changing any UI

control would then refresh the Dossier sub-request for that special agent).
Also, if the Dossier has tabs for each special agent, each tab can have a UI control specific to the special agent for the tab.
42. Semantic Management of Information Stores The Information Nervous SystemTM can also be used to manage information stores such as personal email inboxes, personal contact lists, personal event calendars, a desktop file-system (e.g., the Microsoft Windows Explorer file-management system for local and network-based files), and also other stores like file-shares, content management systems, and web sites.
For client-based stores (such as email inboxes and file-systems), the client runtime of the semantic browser should periodically poll the store via a programmatic interface to check for items that have become redundant, stale, or meaningless. This would address the problem today where email inboxes keep growing and growing with stale messages that might have "lost their meaning and relevance." however, due to the sheer volume of information users are having to cope with, many computer users are losing the ability to manage their email inboxes themselves, resulting in a junk-heap of old and perhaps irrelevant messages that take up storage space and make it more difficult to find relevant messages and items.
The client runtime should enumerate the items in the user's information stores, extract the concepts from the items (e.g., from the body of email messages and from local documents) and send the concepts to the KISes in the user's profiles. In an alternative embodiment, only the default profile should be used. The client then essentially "asks" the user's subscribed I~ISes whether the items mean anything to them. In the preferred embodiment, the client should employ the following heuristics:
1. First, check for redundancy - by flagging (or deleting) duplicate email items, duplicate documents that share concepts and summaries (but perhaps with different titles or file-sizes). The client should either delete the duplicate items (user-configurable) or flag the items by moving them into a special folder (user-configurable) in the email client or desktop.
2. Next, for non-duplicate items, the client should check for meaninglessness or irrelevance. First, the client should only check items that are "older" than N
days (e.g., 30 days) by examining the last-modified time of the email item, document, or other object. For items that qualify, extract the concepts and call the XML Web Service for each I~.IS in all the user's profiles (or the default profile in an alternative embodiment).
3. For very old items (e.g., older than 180 days), the client should specify a very low threshold of meaning to the XML Web Service (e.g., 25%) for preservation.
Essentially, this is akin to deleting (or flagging) those items that are very old and weak in meaning.
4. For fairly old items (e.g., older than 90 days old but younger than 180 days old), the client should specify a very low threshold (e.g., 10%) for preservation.
This is akin to deleting (or flagging) those items that are fairly old and very weak in meaning.
5. For old items (but not too old - e.g., older than 1 day old but younger than 30 days old), the client should specify a very low threshold (e.g., 0%) for preservation. This is akin to deleting (or flagging) those items that are old (but not too old) but are meaningless, based on the user's profile(s).
Essentially, the model fox this aspect or feature of the preferred embodiment balances semantic sensitivity with time-sensitivity by imposing a higher semantic threshold on younger items (thereby preserving items that might be largely - albeit not totally -meaningless if they are fairly young. For example, fairly recent email threads might be very weak in meaning - the client should preserve them anyway because their "youth" is also a sign of relevance. As they "age," however, the client can safely delete them (or flag them for deletion).
This model can also be applied to manage documents on local fzle-systems. The model can be extended to content-management systems, document repositories, etc. by configuring an Information Store lVIonitor (IS1VI) to monitor these systems (via calls to the Information hTervous SystemT~ ~I~L Web Services) and configuring the ISIS! with I~ISes that are configured with KI~Ses that have ontologies consistent with the domain of the repositories to be semantically managed. This feature will save storage space and storage/maintenance costs by semantically managing content management systems and ensuring that only relevant items get preserved on those systems over time.
43. Slide-Rule Filter User Interface The refinement pane in the semantic browser allows the user to "search within results."
The user will be able to add additional keywords, specify date ranges, etc.
The date-range control can be implemented like a slide-rule. Shifting one panel in the slide-rule would shift the lower date boundary while moving the other panel will shift the upper date boundary. Other panels can then be added for time boundaries - shifting both time and date panels will impose both date and time constraints. Panels can also be added for other filter axes.
C. SERVER-SIDE SEMANTIC QUERY PROCESSOR SPECIFICATION
1. Overview This section describes a currently preferred embodiment of how the server-side semantic query processor (SQP) resolves SQML queries. Ori a given server, queries can be broken into several components:
a. Context (documents, keywords, entities, portfolios (or entity collections)).
b. Context/Knowledge Template (or Special Agent) or Information Template -this describes whether the request if for a knowledge type (e.g., Breaking News, Conversations, Newsmakers, or Popular Items) or for a particular information type (e.g., Documents, Email).
On the client, a semantic query is made up of the triangulation of context, request (or Agent) type, and the knowledge communities (or Agencies). The client sends the SQML that represents the semantic query to all the knowledge communities in the profile in which the request lives. The client asks for a few results at a time and then aggregates the results from one or more servers.
The server-side semantic query processor subdivides semantic queries into several sub-queries, which it then applies (via SQL inner joins or sub-queries in the preferred embodiment).
These sub-queries are:
1. Request type sub-query - this represents a sub-query (semantic or non-semantic) depending on the request type. Examples are context (knowledge) types (e.g., All Bets, Best Bets, Headlines, Experts, etc.) and information types (like General Documents, Presentations, Web Pages, Spreadsheets, etc.), 2. Semantic context sub-query - this represents a semantic sub-query derived from the context (filter) passed from the client (an example of this is categories sent from the client or mapped from keywords/text via semantic stemming).
3. Non-semantic context sub-query - this represents a non-semantic sub-query derived from the context (filter) passed from the client (examples are keywords without semantic stemming - mapping to ontology-based categories).
4. Access-control sub-query - this represents a sub-query that filters out those items in the semantic metadata store (S1YIS) that the calling user does not have access to. For details, see the "Security" specification.

The foregoing steps are illustrated in Figure 14 (Server-Side Semantic Query Processor Components). Figure 14 shows how the server-side semantic query processor processes incoming semantic queries (represented as SQML).
2. Semantic Relevance Score The semantic relevance score defines the normalized score that the concept extraction engine returns. It maps a given teen of "blob" of text to one or more categories for a given ontology. The score is added to the semantic network (in the "LinkStrength"
field of the "SemanticLinks" table) when items are added to the Semantic Network.
3. Semantic Relevance Filter The relevance filter is different from the relevance score (indeed, both will typically be combined). The relevance filter indicates how the SQP will semantically interpret context (note:
in the currently preferred embodiment, the filtering is always semantic in this case). There are two relevance filters: High and Low. kith the High relevance filter, the SQP
will include a sub-query that is the intersection of categories and terms. For instance, context for the keyword "XML" will be interpreted as: Items that share the same categories as XML and also include the keyword "XML." This is the highest level of ontology-based semantic filtering that can occur.
However, it could lead to information loss in cases where there axe objects in the Semantic Network (or Semantic Metadata Store (SMS)) that are semantically equivalent to the context but that do not share its keywords or terms. For instance, the query described above would miss items that share the same categories as XML but which include the term "Extensible Markup Language" instead. A Low relevance filter will only include obj ects that share the same categories as the context but unlike the High relevance filter, would not include the additional constraint of lceyword equivalence.
For this reason, the relevance filter is preferably used only to create sub-query "buckets"
that are then used for ordering results. For instance, the SQP might decide to prioritize a High relevance filter ahead of a Low relevance filter when filtering the semantic network but would still return both (with duplicates removed) in order to help guarantee that synonyms don't get rejected during the final semantic filtering process.
4. Time-Sensitivity Filter The time-sensitivity filter determines how time-critical the semantic sub-query is. There are two levels: High and Low. A High filter is meant to be extremely time-critical. Default is 3 hours (this accounts for lunch breaks, time away from the office/desk, etc.).
A Low filter is meant to be moderately time-critical. The default is 12 hours.
S. Knowledge Type Semantic Query Implementations Throughout this application certain specific knowledge types axe referred to by apt shorthand names, some of which the applicant uses or may use as trademarks.
This section explains the nature and function of some of these in greater detail.
a. All Bets For "All Bets" queries, the serer simply returns all the items in the semantic metadata store. If the SQML has filters, the filters are imposed via an inner sub-query with no semantic link strength threshold. For instance, All Bets on Topic A will return all items that have anything (strongly or barely) to do v~rith Topic A.
b. candor, Bct~
In the preferred embodiment, for "Random Bets" queries, the server simply returns all the items in the semantic metadata store (like in the case of "All Bets" queries) but orders the results randomly. If the SQML has filters, the filters are imposed via an inner sub-query with no semantic link strength threshold. For instance, Random Bets on Topic A will return all items (ordered randomly) that have anything (strongly or barely) to do with Topic A.
c. Breaking News If the serer has user-state, Breaking News can be implemented in a very intelligent way.
The table below illustrates the currently preferred ranking and prioritization for Breaking News when the server tracks what items (and/or categories) the user has read:

Priority Sub-Query Time- Semantic Primary Secondary Name SensitivityRelevance Ordering Ordering Filter Filter Axis Axis 1 Breaking Low High Creation Semantic Unread Time Relevance Semantic Score News 2 Breaking Low Low Creation Semantic Unread Time Relevance Semantic Score' News 3 Breaking High High Creation Semantic Read Time Relevance Semantic Score News 4 Breaking High Low Creation Semantic Read Time Relevance Semantic Score News W the preferred embodiment, the server processes SQML for Breaking News (via the Breaking News content predicate) as follows:
I. All breaking news is filtered with a sub-query that the returned news must be "younger" than N hours (or days, or months, configurable) - this imposes the key time-sensitivity constraint.
2. Breaking News is always semantic.
3. In the preferred embodiment, the Semantic Network Manager (SNM) should update the semantic network to indicate the "last read time" for each user to each category. This is then used in the sub-query to check whether news has been "read" or not (per category or per object - per category is the preferred embodiment because the latter will not scale).
4. Priority is given to news items that the user has not "read" (this is implemented by comparing the last read time in the SemanticLinks table with the semantic link type that links "User" to "Category").
5. The implication of the semantic prioritization scheme is that the user could get "older" breaking news first because the news is more semantically relevant and "younger"

breaking news "later" because the news is less semantically relevant. This results in a hybrid relevance-time sensitivity prioritization scheme.
6. The primary ordering axis (Creation Time) guarantees that results are filtered by freshness. The secondary ordering axis (Relevance Score) acts as a tiebreaker and guarantees that equally fresh results are distinguished primary based on relevance.
7. Breaking News Intrinsic Alerts can be implemented on the client by limiting the Breal~ing News priority to Priority 2 and by changing the Priority 1 and Priority time-sensitivity filters to high. This way, only very fresh Brealcing Unread Semantic News (of both High and Low semantic relevance filters) will be returned. This is advantageous because the alert should have a higher disruption threshold than the Breaking News Request (or agent) -since it is implicit rather than explicit.
8. Unread Breaking News is higher priority than Read Breaking News because users are likely to be more interested in stuff they haven't seen yet.
9. Unread Breaking News has a lower time-sensitivity filter than Read Breaking News because users are likely to be more tolerant of older news that is new to them than younger news that is not.
In some cases, the server might not have user-state (and "iead'9 information).
In this case9 a simple implementation of Breaking News is shown below:
1. By default (no filter), Breaking News should return only items younger than N
hours (default is 3 hours).
2. If there is at least one filter in the SQML, Breaking News should apply the time-sensitivity filter (3 hours) to the outer sub-query and also apply a moderately strong relevance filter to the inner sub-query (off the SemanticLinks table). In the preferred embodiment, this should correspond to a relevance score (and link strength) of 50%. For instance, Breaking News on Topic A should return those items that have been posted in the last 3 hours and which belong to the category (or categories) represented by Topic A with at least a relevance score of 50%.
This will avoid false positives like Breaking News items which are barely relevant to Topic A.

d. Headlines Ditto with Breaking News (except that time-sensitivity constraints are more relaxed -e.g., the High filter is 12 hours instead of 3 hours and the low filter is 1 day instead of 12 hours).
In the simple implementation, the time-sensitivity constraint is 1 day. This can also be made 3-days on Mondays to dynamically handle weekends (making the number of days the "number of working days").
e. Newsmakers Newsmakers are handled the same way as Headlines, except that the SQP returns the authors of the Headline items rather than the items themselves.
f. Best Bets As described in my parent application (Serial No.lO/179,651), Best Bets axe implemented by imposing a filter on the strength of the semantic link with the "Belongs to Category" predicate. The preferred default is 90%, although the client (at the option of the user) can change this on the fly via an argument passed via the XML Web Service.
Best Bets are implemented with a SQL inner join between the Objects table and the SemanticLinks table and joining only those rows in the SemanticLinlcs table that have the "Belongs to Category" predicate and a LinkStrength greater than 90% (default). When the SQML that is being processed contains filters (e.g., keywords, text, entities, etc.), the server-side semantic query processor must also involve a sub-query, which is a SQL inner join that maps to the desired alters. In the preferred embodiment, this sub-query should also include a "Best Bets" filter.
In the preferred embodiment, it is advantageous and probably preferable for most users for the outer sub-query to be a Best Bet, and for the inner sub-query. To illustrate this, "Best Bets on Topic A" is semantically different from "Best Bets that are also relevant to Topic A." In the first example, only Best Bets, which are Best Bets "ON" Topic A, will be returned (via applying the "Best Bets" semantic filter on the inner sub-query). In contrast, the second example will return Best Bets on anything that might have anything to do with Topic A.
As such, the second example might return false positives because for example, a document, which is a Best Bet on Topic B but a "weak bet" on Topic B, will be returned and that is not consistent with the semantics of the query or the presumably desired results. Extending the "Best Bets" filter to not only the outer sub-query but also all inner sub-queries will prevent this from happening. Other query implementations can also follow this rule (with the right sub-queries applied based on the semantics of the main query) if the SQML contains filters.
g. Query Implementation for Other Knowledge Types Other knowledge types are implemented in a similar fashion as above (via the right predicates). Several examples are described below.
Information Type Semantic Query Implementations All information type semantic query implementations can follow, and preferably (but not necessarily) follow, the same pattern: the SQP returns only those objects that have the object type id that corresponds to the requested information type. An example is "Information Type~Presentations." When the SQP parses the SQML received from the client, it extracts this attribute from the SQML and maps it to an object type id. It then invokes a SQL query with an added filter for the object type id. For special information types that could span several individual information types (such as "lmformation Type~All Documents"), the SQP maps the request to a set of object type ids and invokes a SQL query with this added filter.
Context Semantic Query Implementations When the client sends SQML that contains concepts (extracted on the client from text or documents), the server-side SQP has to first semantically interpret the context before generating sub-queries that correspond to it. To do this, the server sends the concepts to all KDS'es (I~BS'es) it is configured with (for the desired knowledge community or agency) for semantic categorization. When the server gets the categories back, it preferably determines which of those categories are "strong" enough to be used as filters before generating the appropriate sub-queries.
This "filter-strength" determination is advantageous because if the context is, for example, a fairly long document, that document could contain thousands of concepts and categories. As a result, the "representative semantics" of the document might be contained in only a subset of all the concepts/categories in the document. Mapping all the categories to sub-queries will return results that might be confusing to the user - the user would likely have a "sense" of what the document contains and if he/she sees results that are relevant to some weak concepts in the document, the user might not be able to reconcile the results with the document context. Therefore, in the preferred embodiment, the server-side SQP
preferably chooses only "strong categories" to apply to the sub-queries. It is recommended that these be categories with a semantic strength of at least 50%. That way, only those categories that register strongly in the semantic context would be applied to the sub-query. The implementation of the sub-query would then follow the rules described above depending on whether the query contains a context predicate, is based on a knowledge type, information type, etc.
Semantic Stemming Implementation As described in my parent application, the server-side semantic query processor performs semantic stemming to map keywords, text, and concepts to categories based on one or more domain ontologies. ~ne way it does this by invoking an XML Web Service call to the KDS/KBS (or KDSes/KBSes) it is configured with in order to obtain the categories. It then maps the categories to its semantic network. This form of stemming is superior to regular stemming that is based on keyword variations (such as singular and plural variations, tense variations, etc.) because it also involves domain-specific semantic mapping that stems based on meaning rather than merely stemming based on keyword forms.
In the currently preferred embodiment, the KIS calls the KDS/KBS each time it receives SQML that requires further semantic interpretation. However, this could result in delays if the KDS/KBS resides on a different server, if the network connection is not fast, or if the KDS/KBS
is busy processing many requests. In this case, the ISIS can also implement a Semantic Stemming Cache. Tlus cache maps keywords and concepts to categories that are fully qualified with URIs (making them globally unique). When the server-side semantic query processor receives SQML that contains keywords, text, or concepts (extracted from, say, documents on the client by the client-side semantic query processor), it first checks the cache to see if the keywords have already been semantically stemmed. If there is a cache hit, the SQP simply retrieves the categories from the cache and maps those categories to the semantic network via SQL queries. If there is a cache miss (i.e., if the context is not in the cache), it then calls the KDSesIKBSes to perform semantic categorization. It then takes the results, maps them to unique category URIs, and adds the entry to the cache (with the context as the hash code). Note that even if the context does not map to any category, the "lack of a category" is preferably cached.
In other words, the context is added as a cache entry with no categories. This way, the server can also quickly determine that a given context does not have any categories, without having to call the KDSes/I~BSes each time to find out.
Cache Management The SQP can also manage the semantic stemming cache. It has to do this for two reasons: first, to keep the cache from growing uncontrollably and consuming too much system resources (particularly memory with a heap-based hash table); and, second, if the ISIS
configuration is changed (e.g., if knowledge domains are addedlremoved), the cache is preferably purged because the entries might now be stale. The first scenario can be handled by assigning a maximum number of entries to the cache. In the preferred embodiment, the SQP
caches the current amount of memory consumed by the cache and the cache limit is dictated by memory usage. For example, the administrator might set the maximum cache size to 64MB. To simplify the implementation, this can be mapped to an approximate count of items (e.g., by dividing the maximum memory usage by an estimate of the size of each cache entry).
For each new entry, if the cache limit has not been reached, the SQP simply adds the entry to the cache. However, if the cache limit has been reached, the SQP (in the preferred embodiment) should purge the least recently added items from the cache. In the preferred embodiment, this can be implemented by keeping a queue of items that is kept in sync with a hash table that implements the cache itself (for quick lookups using the context as a key). When the SQP needs to purge items from the cache to free up space, it de-queues an item from the least-recently-added queue and also removes the corresponding item from the hash table (using the context as key). This way, fresh items are more likely to result in a cache hit than older items. This will result in a faster user experience on the client because context for saved agents/requests/queries will end up being cached with quick-lookups each time the user opens the agent/request/query. The same goes for Dossier (Guide) queries which will have the same context (but with different knowledge types) - the client will request for each knowledge type for the same context and since the context will be cached, each sub-query will execute faster.
D. EXTENSIBLE CLIENT-SIDE USER PROFILES SPECIFICATION FOR THE
INFORMATION NERVOUS SYSTEM
Overview Extensible client-side user profiles allow the user of a semantic browser to have a different state for different job roles, knowledge sources, identities, personas, work styles, etc.
This essentially allows the user to create different "knowledge worlds" for different scenarios.
For instance, a Pharmaceuticals researcher might have a default profile that includes all sources of lazowledge that are relevant to his/her work. .As described in my parent application Serial No. 10/179,651, the SRML from each of these sources will be merged on the client thereby allowing the user to seamlessly go through results as though they were coming from one source.
~Iowe~rer, the researcher rrught e~~an~ to tracl~ patents separate from everything else. In such a case, the researcher would be able to create a separate "Patents" profile and also include those knowledge communities (agencies) that have to do with patents (e.g.,. the US
Patent Office Database, the EU Patent Database, etc.) _ To take another example, for instance, the user might create a profile for 'Work' and one for 'Home.' Many investment analysts track companies across a variety of industries. With the semantic browser, they would create profiles for each industry they track.
Consultants move from project to project (and from industry to industry) and might want to save requests and entities created with each project. Profiles will be used to handle this scenario as well.

Profiles contain the following user state:
~ NamefDescription - the descriptive name of the profile.
~ One or more knowledge communities (agencies) that indicate the source of knowledge (running on a KIS) at which requests (agents) will be invoked.
~ Identity Information - the user name (currently tagged with the user's email address) and password.
~ Areas of Interest or Favorite Categories - this is used to suggest information communities (agencies) to the user (by comparing against information communities with identical or similar categories) and as a default query filter for requests created with the profile.
~ Smart styles - the smart styles to be used by default for requests and entities created with the profile.
~ Default Flag - this indicates whether the profile is the default profile.
The default profile is initiated by default when the user wishes to create requests and entities, browse information communities, etc. Unless the user explicitly selects a different profile, the default profile gets used.
Profiles can be created, deleted, modified, and renamed. However, in the preferred embodiment the default profile cannot be deleted because there has to be at least one profile in the system at all times. In alternate embodiments, a minimum profile would not be required.
Preferably, all objects in the semantic browser are opened within the context of a profile.
For instance, a smart request is created in a profile and at runtime, the client semantic query processor will use the properties of the profile (specifically the subscribed knowledge communities (agencies) iai that profile) to u~rol~e the request. This allovJS
a user to correlate or scope a request to a specific profile based on the knowledge characteristics of the request (more typically the sources of knowledge the user wants to use for the request).
Figure 15 illustrates the semantic browser showing two profiles (the default profile named "My Profile" and lSAand a profile named "Patents" 15B). Observe how the user is able to navigate hisJher knowledge worlds via both profiles without interference.
Figures 16A-C illustrate how a user would configure a profile (to create a profile, the user will use the "Create Profile Wizard" and the profile can then be modified via a property sheet as shown).
Figure 17 shows how a user would select a profile when creating a request with the "Create Request Wizard."

E. SMART STYLES SPECIFICATION FOR THE INFORMATION NERVOUS
SYSTEM
1. Smart Styles Overview A color theme and animation theme applied to a style theme yields a "smart style".
"Smart" in this context means the style is adaptive or responsive to the mood of its request, context panes, preview mode, handheld mode, live mode, slideshow mode, screensaver mode, blenderlcollection mode, accessibility, user settings recognition, and possibly other variables within the system (see below). There is an infinite number and kind or "Classes" of possible styles. The preferred embodiment comprises at least the following style Classes:
1. Subtle - for task-oriented productivity.
2. Moderate - for task-oriented productivity with some presentation effects.
3. Exciting - exciting effects (good for both primary and secondary machines, and for inactive Nervana windows - e.g., Nervana client windows in the background or docked on the taskbar).
4. Super-exciting (great for smart screensavers with productivity - e.g., secondary machines - when the user is using hislher primary machine).
Sci-Fi (for Matrix fans, great for smart screensavers without specific need for productivity - e.g., when the user is away from his/her desk).
Style, Color & Animation Themes - Variable, unlimited - created by Nervana, and perhaps users and/or third party skin authors Iaraplieit and I~ynaanic smart ~t~rle JPr~pertie~
a. Mood - the smart style must convey the mood of the request (i.e., the request is a parameter passed to the smart style). This will involve semantic images, semantic motions, Visualizations, etc. that convey the semantically informed or semantically determined properties of the smart request (the context template or infornzation type, the categories, whether there are filters (e.g., local documents), the information types of those filters, etc.) b. Context panes - e.g., deep info pane (per object), dockable preview panes, dockable contextual PIP watch groups/panes, etc.
c. Preview Mode - each smart style must be able to display its results for preview (in a small window).

d. Handheld Mode - each smart style must be able to display its results optimized for a handheld device.
e. Live mode - each smart style must have a "live" mode during which it would display real-time semantic Visualizations (per object). This can be toggled on or off (e.g.
if the user does not want real-time semantic Visualizations, or to save bandwidth that results from real-time Web service calls per object).
f. Slideshow mode - preferably, each smart style must be able to "play" the results of the request - like a live stream.
g. Screensaver mode - preferably, each smart style must be able to "play"
the results of the request as a screensaver. This is a variant of slideshow mode, except in full-screen/theater mode.
h. Blender/collection mode - preferably, each smart style must change its UI
appropriately if the request it is displaying is a blender/colleetion.
i. Accessibility - preferably, each smart style must support accessibility.
j. User settings recognition - the Nervana Librarian will allow users to indicate whether they are beginners, moderate users, or power-users, and their respective job function~s~ (F~~I~, sales, marketing, executive, etc.). Preferably, each smart style considers (or is influenced by) these functions where appropriate.
~ Preferably, each smart style is responsible, consistent with the semantics of the request, for recognizing (or discerning or perceiving) and then Visualizing (or presenting or depicting or illustrating, consistent with what should deserve the user's attention):
~ the Mood of the Current Request (including semantic images, motion, chrome, etc.
~ a Change in the number of Items in the Current Request ~ the Mood of each object (intrinsically) ~ the Mood of each object's context (headlines, breaking news, experts, etc.) ~ Binary/Absolute issues or characteristics (e.g., is there breaking news, OR
NOT? how many experts are there? how many headlines?) as distinct from issues that are matters of degree, or on a gradient or continuum If the characteristic is on a gradient or continuum, perceiving the relative placement along it (e.g., how breaking is breaking news?, how critical are the headlines? what is the level of expertise for the experts?, etc.) ~ a change in each object's context (there is new breaking news, there are new annotations, etc.) ~ the RELATIVE criticality of each object being displayed (different sized view ports, different fonts, different chrome, etc.) ~ a request navigation and "loading" status (interstitials that INTRODUCE the mood of the new request being loaded) ~ all properties of any individual PIP windows (animated with an animation control) ~ the addition of a new PIP window (to a PIP window palette) ~ any Resizing/Moving/Docking PIl' Windows ~ any preview windows (for context palettes, "Visualization UI" on each object, timelines, etc.) ~ Sounds consistent with all of the foregoing Visualizations of mood and notifications (across the board) Figure 18 shows a screenshot with the 'Smart Styles' Dialog Box illustrating some of the foregoing operations and features. As can be seen, the Dialog Box allows the user to browse smart styles by pivoting across style classes, style themes, color themes, and animation themes.
A preview window shows the user a preview of the currently selected smart style.
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1. Overview Smart Request Watch refers to a feature of the Information Nervous System that allows users of the semantic browser (the Information Agent or the Librarian) to monitor (or 6~avatch'9) smart requests in parallel. This is a very advantageous feature in that it enhances productivity by allowing users to traclc several requests at the same time.
. The feature is implemented in the client-side semantic runtime, the semantic browser, and skins that allow a configurable way of watching smart requests (via a mechanism similar to "Picture-In-Picture" (PIP) functionality in television sets). Preferably, one or more of the following software components are used:
1. The Request Watch List (RWL) 2. Request Watch Groups 3. The Notification Manager (NM) 4. Watch Group Monitors (WLM) 5. The Watch Pane 6. The Watch Window 7s 2. Request Watch Lists (RWLs) and Groups (RWGs) The Request Watch List is a list of smart requests (or smart agents) that the client runtime manages. This list essentially comprises the smart requests the user wishes to monitor. The Request Watch List comprises a list of entries, the Request Watch List Entry (RWLE) with the following data structure:
Field Name Field Field Description T a Re uestlD GUID The unique identifier of the smart request Notification ReferenceDWORD The reference count indicating whether the Count Notification Manager should track whether there are "new" obj ects for this smart re uest RequestViewInstancelDGUID The unique identifier of the ~ smart request view instance that "owns" the RWLE. This is used for dynamically added and browser-instance-specific RWLEs like Categorized Headlines, Breaking News, and Newsmakers (see below). For system-wide RWLEs added manually by the user or via non-categorized Request Watch Rules (RWRs) (see below), this entry is initialized to NULL.

LastUpdateTime Date/TimeThe Iast date/time the notification manager updated the request results count RequestResultsCount DWORD The number of results in the smart request LastResultTime Date/TimeThe date/time of the most recently published result The Request Watch List (RWL) contains an array or vector of RWLE structures.
The Request Watch List Manager manages the RWL. The semantic browser provides a user interface that allows the user to add smart requests to the RWL - the UI talks to the RWLM to add and remove RWLEs to/from the RWL. The RWL is stored (and persisted) centrally by the client-side semantic nmtirne (either as an XML file-based representation or in a store like the Windows registry).
The RWL can also be populated by means of Request Watch Groups (RWGs). A
Request Watch Group provides a means for the user to monitor a collection of smart requests. It also provides a simple way for users to have the semantic browser automatically populate the RWL based on configurable criteria. There are at least two types of RWGs: Auto Request Watch Groups and the Manual Request Watch Group. Auto Request Watch Groups are groups that are dynamically populated by the semantic browser depending on the selected profile, the profile of the currently displayed request, etc. The Manual Request Watch Group allows the user to manually populate a group of smart requests (regular smart requests or blenders) to monitor as a collection. The Manual Request Watch Group also allows the user to add support context types (e.g., documents, categories, text, keywords, entities, etc.) - in this case, the system will dynamically generate the semantic query (SQML) from the filters) and add the resulting query to the Manual Request Watch Group. This saves the user from having to first create a time-sensitive request based on one or more filters before adding the filters to the Watch Group - the user can simply focus on the filters and the system will do the rest.
Users will be able to add the following types of Auto-RWGs (for one or more configurable profiles, including "All Profiles" as shown in the Smart Request Watch Dialog Box in Figure 19):
1. Breaking News - this tells the semantic browser to automatically add a Breaking News smart request to the RWL (for the selected profile(s)).
2. ~-Ieadlines - this tells the semantic browser to automatically add a Deadlines smart request to the RWL (for the selected profile(s)).
3. Newsmakers - this tells the semantic browser to automatically add a Newsmalcers smart request to the RWL (for the selected profile(s)).
4. Categorized Breaking News - this tells the semantic browser to automatically add Categorized Brealcing News smart requests to the RWL (for the contextual profile). The semantic browser will dynamically add smart requests with category filters corresponding to each subcategory of the currently displayed smart request (and for the contextual or current profile) - if the currently displayed smart request has categories. For example, if the smart request "Breaking News" about Technology" is currently being displayed in a semantic browser instance, and if the category "Technology" has 5 sub-categories (e.g., Wireless, Semiconductors, Nanotechnology, Software, and Electronics), the following smart requests will be dynamically added to the RWL when the current smart request is loaded:
~ Breaking News about Technology.Wireless [<Contextual Profile Name>]
~ Breaking News about Technology.Semiconductors [<Contextual Profile Name>]
~ Breaking News about Technology.Nanotechnology [<Contextual Profile Name>]
~ Breaking News about Technology.Software [<Contextual Profile Name>]
~ Breaking News about Technology.Electronics [<Contextual Profile Name>]
Also, the RWLEs for these entries will be initialized with the RequestViewInstancelD of the current semantic browser instance. If the user navigates to a new smart request, the categorized Breaking News for the previously loaded smart request will be removed from the RWL and a new list of categorized Breaking News will be added for the new smart request (if it has any categories) - a.nd initialized with a new RequestViewInstancelD
corresponding to the new smart request view. This creates a smart user experience wherein relevant categorized breaking news (for subcategories) will be dynamically displayed based on the currently displayed request. The user vrill then be able to monitor Categorized Breaking News smart requests as a watch group or collection.
5. Categorized Headlines - this tells the semantic browser to automatically add Categorized Headlines smart requests to the RWL (for the cor~te~tual profile)o This is similar tea Categorized Breaking News, except that Headlines are used in this case. The user will then be able to monitor Categorized Headlines smart requests as a watch group or collection.
6. Categorized Newsmakers - this tells the semantic browser to automatically 'add Categorized Newsmakers smart requests to the RWL (for the contextual profile).
This is similar to Categorized Breaking News, except that Newsmakers are used in this case.
The user will then be able to monitor Categorized Newsmakers smart requests as a watch group or collection.
7. My Favorite Requests - this tells the semantic browser to automatically add all favorite smart requests to the RWL (for the selected profile(s)). This allows the user to watch or monitor all his/her favorite smart requests as a group.
7s 8. My Favorite Breaking News - this tells the semantic browser to automatically add all favorite breaking news smart requests to the RWL (for the selected profile(s)), This allows the user to watch or monitor all his/her favorite breaking news smart requests as a group.
9. My Favorite Headlines - this tells the semantic browser to automatically add all favorite headlines smart requests to the RWL (for the selected profile(s)).
This allows the user to watch or monitor all his/her favorite headlines smart requests as a group.
10. My Favorite Newsmakers - this tells the semantic browser to automatically add all favorite newsmakers smart requests to the RWL (for the selected profile(s)). This allows the user to watch or monitor all his/her favorite newsmakers smart requests as a group.
Request Watch Group Manager' User Interface Figure 19 illustrates the "Smart Request Watch" Dialog Box in the semantic browser of the preferred embodiment. The top half of the dialog is used to add auto-watch groups. The user can select auto-watch group types and profile types ("I~11 Profiles,"
"Contextual Profile," and the actual profile names) and add them to the auto-watch-group list. The user can also remove auto-watch-groups. The bottom half of the dialog box is used to add/remove smart requests to/from the manual watch group.
'1I'k~c I'~~~aficatf0n I~aa~a~;e~~ (l~tl~) In the preferred embodiment the Notification Manager (NM) is a component of the semantic runtime client that monitors smart requests in the RWL. The NM has a thread that periodically invokes each smart request in the RWL (via the client semantic query processor) and updates the RWLE with the "results count" and the "last update time." In the preferred embodiment the NM preferably invokes the smart requests every 5-30 seconds.
The NM can intelligently adjust the periodicity or frequency of request checks depending on the size of the RWL (in order to minimize bandwidth usage and the scalability impact on the Web service).
For time-sensitive smart requests (like Breaking News, Headlines, and Newsmakers), the NM preferably invokes the smart request without any additional time filter.
However, for non time-sensitive requests (like for information as opposed to context types or for non time-sensitive context templates like Favorites and Recommendations), the NM preferably invokes the query for the smart request with a time filter (e.g., the last 10 minutes).
4. Watch Group Monitors In the preferred embodiment, the semantic runtime client manages what the inventor calls Watch Group Monitors (WGM). For each watch group the user has added to the watch group list, the client creates a watch group monitor. A watch group monitor tracks the number of new results in each request in its watch group. The watch group monitor creates a queue for the RWLEs in the watch group that have new results. The WGM manages the queue in order to maximize the freshness of the results. The WGM periodically polls the NM to see whether there are new results for each request in its watch group. If there are, it adds the request to the queue depending on the 'last result time' of the request. It does this in order to prioritize requests with the freshest results first. The currently displayed visual style (skin) running in the Presenter would then call the semantic runtime OCX to dequeue the requests in the WGM
queue. This way, the request watch user interface will be consistent with the existence of new results and the freshness of the results. ~nce there are no more new results in the currently displayed request, the smart style will dequeue the next request from the WGM queue.
5. The Watch Pane The Watch Pane (WP) refers to a panel that gets displayed in the Presenter (alongside the main results pane) and which holds visual representations of the user's watch groups. The WP
allows the user to glance at each watch group to see whether there are new results in its requests.
The WP also allows the user to change the current view with which each watch group's real-time status gets displayed. The following views are currently defined:
~ Tiled View - this displays the title of the watch group along with the total number of new results in all its smart requests.
~ Ticker View - this displays the total number of new results in all the watch group's smart requests but also shows an animation that sequentially displays the number of new results in each smart request (as a ticker).

~ Preview View - this is similar to the ticker view except that the most recent result per smart request is also displayed alongside the number of new results in the ticker.
~ Deep View - in this view, the WP displays the total number of new results in all the watch group's smart requests along with a ticker that shows the number of new results in each smart request and a slide-show of all the new results per smart request.
6. The Watch Window The WP also allows the user to watch a watch group. The user will do this by selecting one of the watch groups in the WP and dragging it into the main results pane (or by a similar technique). This forms a Watch Window (WW). This WW resembles or can be analogized to TV's picture-in-picture functionality in appearance or layout, but differs in several ways, most noticeably in that in this case the displayed content is comprised of semantic requests and results as opposed to television channels are being "watched." Of course, the underlying technology generating the content is also quite different. The WW can be displayed in asry of the aforementioned views. When the WW is in Deep View however, the WW's view controls are displayed. The following controls are currently defined:
~ Pinning Requests - this allows the user to pin a particular request in the watch group. The WW will keep displaying the new results for only the pinned requests (in a cycle) and will not advance to other requests in the watch group for as long as the current request remains pinned.
~ Swapping Requests - this allows the user to swap the curs ently displayed request ~~ith the main request being shoer~n in the semantic browser. The smart style will invoke a method on the OCX to create a temporary request with the swapped request (hashed by its SQML buffer) and then navigate to that request while also informing the Presenter to now display the main request in its place (in the WW).
~ Stop, Play, Seek, FF, RW, Speedup - these allow the user to stop, play, seek, fast-forward, rewind or speedup the "watch group request stream." For instance, a fast-forward will advance to several requests ahead of the currently displayed one.
~ Results controls - this allows the user to control the results in each request in the watch group. Essentially, the results are a stream within a stream and this will also allow the user to control the results in the current request in the current watch group.
~ Auto-Display Mode - this will automatically hide the WW when there are no results to display and fade it in when there are new results. This way, the user can maximize the utility of hislher real estate on the screen knowing that watch windows will fade in when there are new semantic results. This feature also allows the user to manage his/her attention during information interaction in a personal and semantic way.
~ Docking, Closing, Minimizing, Maximizing - these features, as the names imply, allow the user to dock, close, minimize or maximize watch windows. Figure 20 illustrates a Watch Window displaying Filtered Smart Requests (e.g.,. Headlines on Wireless). Figure 20 is s1 an Illustration of the Watch Window with a Current Smart Request Title (e.g., "Breaking News").
7. Watch List Addendum In the User Interface, the Watch List can be named "News Watch." The user will be asked to add/remove requests, objects, keywords, text, entities, etc. to/from the "News Watch."
The "News Watch" can be viewed with a Newsstand watch pane. This will provide a spatially-oriented view of the user's requests and dynamically-created requests (via objects added to the Watch List, and created dynamically by the runtime using those objects as filters) - not unlike the view of a news-magazine rack when one walks into a Library or Bookstore.
G. ENTITIES SPECIFICATION FOR THE INFORMATION NERVOUS SYSTEM
1. Introduction Entities are a very powerful feature of the preferred embodiment of the Information Nervous System. Entities allow the user to create a contextual definition that maps to how they work on a regular basis. Examples of entities include:
1. People 7. Meetin s 2. Teams ~. ~rganizations 3. Action Items ~. Partners 4. Companies 10. Products 5. Com etitors 11. Projects 6. Customers 12. Topics There are also industry-specific entities. For instance, in pharmaceuticals, entities could include drugs, drug interaction issues, patents, FDA clinical trials, etc.
Essentially, an entity is a semantic envelope that is a smart contextual object. An entity can be dragged and dropped like any other smart object. However, an entity is represented by SQML and not SRML
(i.e., it is a query-object because it has much richer semantics). An entity can be included as a parameter to a smart request.
s2 The user creates entities based on his/her tasks. Entities in the preferred embodiment contain at least the following information (in alternate embodiments they could contain more or less information):
1. Name/Description - a friendly descriptive name for the entity.
2. The categories of the entity - based on standard cross-industry taxonomies or vertical/company-specific taxonomies.
3. Contextual resources - these could include keywords, local documents, Internet documents, or smart objects (such as people).
An entity can be opened in the semantic browses, can be used as a pivot for navigation, as a parameter for a smart request (e.g., Headlines on My Project), can be dragged and dropped, can be copied and pasted, can be used with the smart lens, can be visualized with a smart style, can be used as the basis for an intrinsic alert, can be saved as a .ENT document, can be emailed, shared, etc. In other words, an entity is a first-class smart object.
The semantic runtime client dynamically creates SQML by appending the rich metadata of the entity to the subject of the relational request to create a new rich SQML that refers to the entity.
Entities preferably also have other powerful characteristics:
1. Regarding topicsp entities allow the user to create his/her primate taxonomy (without being at the mercy of or restricted exclusively to a public taxonomy that is strictly defined and as such, might not map exactly to the user's specific context for a request). The problem with taxonomies is that no taxonomy can ever fit everybody's needs -even in the same organization. Context is very personal and entities allow the user to create a personal taxonomy.
For instance, take the example of a dog (of the boxer breed) named Kashmir owned by a dog-owner Steve. To everyone else (but Steve), Kashmir can be expressed (taxonomically) as:
Living Things Animals Mammals Dogs Boxers Kashmir But to Steve, Kashmir is also:
My Loved Ones My Pets Kashmir To Steve's veterinary doctor, however, Kashmir is:
My Clients My Dogs My Dogs in Good Health Kashmir If taxonomies (standalone) were used to "define" Kashmir, none of the three taxonomies would satisfy the general public, Steve, and Steve's veterinary doctor. With entities on the other hand, Steve could create a "Kashmir" entity based on "what Kashmir means to him." Everyone else could then do the same. And so can Steve's veterinary doctor. Entities therefore empower the user with the ability to create private topics that might be extensions of broad taxonomies.
To take another example, a Phamnaceuticals researcher in a large Pharmaceutical company might be working on a new top-secret project (named "Gene Project") on Genomics.
Because "Gene Project" is an internal project, it would likely not exist in a public taxonomy vrhich could be used vrith the senmntic browser of this the preferred embodiment of my invention. However, the researcher could create an entity named "Gene Project", typed as a Proj ect, and could then initialize the entity by scoping it to Genomics (which exists in broad taxonomies) and then also qualifying it with the keyword-phrase "Gene Project"
(using the AND
operator). Essentially, this is akin to defining "Gene Project" as anything on Genomics that has the phrase "Gene Project." This will impose much stricter context than merely using the keywords "Gene Project" (which might return results that contain the word "Project" but have nothing to do with Genomics). By defining a personal topic, "Gene Project"
that is scoped to Genomics but also extends "Gene Project" with a specific qualifier, the researcher now has much more precise and personal context. The entity can then be dragged and dropped, copied and pasted, etc. to create requests (e.g., "Experts on Gene Project." At runtime, the server-side semantic query processor will interpret this (by mapping the SQML to the semantic network) as "Experts on any information that belongs to the category Genomics AND which also includes the phrase "Gene Proj ect."
2. Entities also allow the user to create a dynamic taxonomy - public taxonomies are very static and are not updated regularly. With entities, the user can "extend" his/her private taxonomy dynamically and at the speed of thought. Knowledge is transferred at the speed of thought. Entities allow the user to create context with the same speed and dynamism as his/her mind or thought flow. This is very significant. For instance, the user can create an entity for a newly scheduled meeting, a just-discovered conference, a new customer, a newly discovered competitor, etc. - ALL AT THE SPEED OF THOUGHT. Taxonomies don't allow this.
3. Taxonomies assume that topics are the only source of context. With entities, a user can create abstract contextual definitions that include - but are not limited to - topics.
Examples include people, teams, events, companies, etc. Entities might eventually "evolve" into topics in a taxonomy (over time and as those entities gain "fame" or "notoriety") but in the "short-term," entities allow the user to create context that has not yet evolved (or might never evolve) into a full-blown taxonomic entry. For instance, Nervana (our company) was initially an entity (known only to itself and its few employees) but as we have grown and attracted public attention, as an entity we are evolving into a topic in a public taxonomy.
With entities, users don't have to wait for context (like Nervana) to "eventually become" topics.
4. Entities allow the user to create what the inventor calls "compound context." An example of this is a meeting. A meeting typically involves several participants with documents, presentation slides, and/or handouts relevant to the topic of discussion. With entities in the Information Nervous System, a user can create a "meeting" context that captures the semantics of the meeting. Using the Create Entity Wizard, the user can specify that the entity is a meeting, and then specify the semantic filters. Consider an example of a project meeting with five participants and 2 handed out documents, and one presentation slide. The Presenter of the meeting might want to create an entity in order to track knowledge specifically relevant to the s5 meeting. For instance, helshe might want to do this to determine when to schedule a follow-up meeting or to track specific action items relating to the meeting. To create the entity, the user would add the email addresses of the participants, the handed out documents, and also the presentation to the entity filter definition. The user then saves the entity which is then created in the semantic namespacelenvironment. The user can then edit the entity with new or removed filters (and/or a new name/description) at a later dateltime - for instance, if helshe has discovered new documents that would have been relevant to the meeting. When the user drags and drops the entity or includes it in a requestlagent, the semantic browser then compiles the entity and includes it in a master SQML with the sub-queries also passed to the XML Web Service for interpretation. The server-side semantic query processor then processes the compound SQML
by constructing a series of SQL sub-queries (or an equivalent) and by joining these queries with the entity sub-queries which in turn are generated using SQL sub-queries.
The user can use an ANh or OIL (or other) operator to indicate how the entity filters should be applied. For instance, the user can indicate that the meeting (semantically) is the participants of the meeting AND the documentslslides handed out during the meeting. When the entity is compiled at the client and the server, the SQML equivalent is used to interpret the entity (vrith the desired operatorj. This is very powerful. It means that the user can define an entity named "Project Meeting" and drag and drop that entity to the special agent named "Breaking News." This then creates a request named "Breaking News on Proj ect Meeting"
(with the appropriate SQML referring to the identifier of the entity- which will then be compiled into sub-SQML before it is passed to the servers) for interpretation. The server then applies default predicates to the entries in the entity (based on what "makes sense" for the object). In this particular example, because of the definition of the entity, the server will then only return:
Breaking News BY ALL the participants AND which is ALSO semantically relevant TO
ALL the documents/slides For instance, this will only return conversations/threads that involve all the participants of the meeting and which are semantically relevant to all the handouts given out during the meeting.

This is precisely what the user desired (in this case) and the semantic browser would have empowered the user to essentially construct a rather complex query.
Even more complex queries are possible. Entities can include other entities to allow for compound entities. For instance, if an entire team of people were involved in the meeting, the Presenter might want to create an entity that includes an email distribution list of those people.
In this case, the user might search the Information Nervous System for the distribution list and then save the result as an entity. The browser will allow the user to save results as entities and based on the result type, it will automatically create an entity with a default entity type that "makes sense." For instance, if the user saves a document result as an entity, the semantic browser it will create a "Topic" entity. If the user saves a Person result as an entity, the semantic browser will create a "Person" entity. If the user saves an email distribution list as an entity, the semantic browser will create a "Team" entity.
In this example, the user can save a Person result as a Person entity and then drag and drop that entity into the Project Meeting entity. The Team entity that maps to the email distribution list of the meeting participants can be dragged and dropped to the Project Meeting entity. The user can then create a request called "Deadlines on Project Meeting" that includes the entity. The semantic query processor will then return Deadlines B~ anyone in the email distribution list (using the right default predicate) and which is semantically relevant to ALL the handouts given out during the meeting. Similarly, a ossier (Guide) on the Project Meeting will return All Bets on the meeting, Best Bets on meeting, Experts on the meeting, etc.
Note that such a compound entity that includes other entities gets checked by the client-side semantic consistency checker for referential integrity. In other words, if Entity A refers to Entity B and the user attempts to delete Entity B, the semantic browser will detect this and flag the user that Entity B has an outstanding reference. If the user deletes Entity B anyway, the reference in Entity A (and any other references to Entity B) will get removed.
Alternately, in some embodiments, the user could be prohibited (whether informed or not) from deleting Entity B in the same situation, based on permissions of others within an organization associated with s7 the entity. For example, employers could monitor activities of employees for risk management purposes, like as is done with email in some companies, only much potentially much more powerfully (Of course, appropriate policies and privacy considerations would have to be addressed). The same process applies to Request Collections (Blenders), Portfolios (Entity Collections - see below), and other compound items in the semantic namespace/environment (items that could refer to other items in the namespace/environrnent).
5. Popular entities can also be shared amongst members of a knowledge community.
Like other items in the semantic browser (like requests or knowledge communities (agencies), entities can be saved as files (so the user can later open them or email them to colleagues, or save them on a central file share, etc.). A common scenario would be that the corporate Librarians at businesses would create entities that map to internal projects, meetings, seminars, tasks, and other important corporate knowledge items of interest. These entities would then be saved on a file-share or other sharing mechanism (like a portal or web-site) or on a knowledge community (agency). The knowledge workers in the organization would then be able to use the entities. As the entities get updated, in the preferred embodiment the Librarians can and will automatically edit their context and users will be able refresh or synchronize to the new entities. Entities could also and alternately be shared on a peer-to-peer basis by individual users.
This is akin to a legal peer-to-peer file sharing for music, but instead of music, what is shared is context to facilitate meaning, or more meaningful communication.
2. Portfolios (or Entity Collections) Portfolios are a special type of entity that contains a collection of entities. In the preferred embodiment, to minimize complexity and confusion (at least of nomenclature or terminology), while an entity can be of any size or composition, and portfolio can contain any kind or number of entities, a portfolio would not contain other portfolios. A portfolio allows the user to manage a group of entities as one unit. A portfolio is a first-class entity and as such has all the aforementioned features of an entity. When a portfolio is used as a parameter in a smart request, ss the OR qualifier is applied (by default) to its containing entities. W other words, if Portfolio P
contains entities E1 and E2, a smart request titled 'Headlines on P' will be processed as 'Headlines on E1 or E2.' The user can change this setting on individual smart requests (to an AND qualifier).
3. Sample Scenarios Again, in reviewing the scenarios below, it is helpful to recall that, conceptually, the system can gather more relevant information in part because it "knows" who is asking for it, and "understands" who that person or group is, and the kinds of information they are probably interested in. Of course, strictly speaking, the system is not cognitive or self aware in the full human sense,' and the operative verbs in the preceding sentence are conceptual metaphors or similes. Still, in operation and results, it mimics understanding and knowledge to an unprecedented degree in part because of its underlying semantically-informed architecture and execution.
This point can be illustrated by a simplistic contrast: If two very different people entered the exact same search at the exact same time into a search engine such as Google, they would get the exact same results. In contrast, with the preferred embodiment of the present system? if those same two people entered the same request via an Entity, each would get different results tailored to be relevant to each.
To appreciate some of the potential power of this feature, it is useful to note that while the system or Entities "kn.ow" who is posing the query, the Entities do not depend for that k~lowledge on the user informing them and keeping them constantly updated and informed (although user information can be supplied and considered at any time). Tf that were the case, the system could be too labor intensive to be efficient and useful in many situations; it would just be too much work. Instead, the Entities "know" who the requester is by inference and from semantics from characteristics sometimes supplied by others, sometimes derived or deduced, sometimes collected from other requests and the like, as explained throughout tlus application and its parent application.
Some example scenarios of Entities in operation:
1. A pharmaceuticals 'patent' entity could include the categories of the patent, relevant keywords, and relevant documents.
2. A CIA agent could create a 'terrorist' entity to track terrorists. This could include categories on terrorism, suspicious wire transfers, suspicious arms sales, classified documents, keywords, and terrorism experts in the information community.
3. Find All Breaking News on Yesterday's Meeting.
4. Find Headlines on any of my competitors (this is done by creating the competitor entities, and then creating a smart request with the entities as parameters using the OR qualifier with each predicate).
5. Find Experts on my investment portfolio companies (create the individual entities, create a portfolio containing these entities and then create a smart request that has the 'Experts' context template and that uses the portfolio as an argument).
6. Open a Dossier (Guide) on my competitors (create the individual competitor entities, create a portfolio containing these entities and then create a smart request that has the 'Dossier' (or 'Guide') context template and that uses the portfolio as an argument). Figure 21 shows Entity views displayed in the semantic browser (on the left).
H. KNOWLEDGE COMMUNITY BROWSING AND SUBSCRIPTIOI~T
SPECIFICATION FOR THE INFORMATION NERVOUS SYSTEM
Overview The IVer~Jam semantic bro~~ser will allovJ the user to subscribe and unsubscribe to/from knowledge communities (agencies) for a given profile. These lmowledge communities will be readily available to the user underneath the profile entry in the semantic environment. In addition, these knowledge communities will be queried by default for intrinsic alerts, context panels, and etc. whenever results are displayed for any request created using the same profile.
The semantic environment includes state indicating the subscribed knowledge conununities for each profile. The client-side semantic query processor (SQP) uses this information for dynamic requests that start from results for requests of a given profile (the SQP
will ask the semantic runtime client for the knowledge communities for the profile and then issue XML Web Service calls to those knowledge communities as appropriate).

Figures 22A and 22B show the user interface for the knowledge community subscription and un-subscription. The dialog box has combo boxes allowing the user to filter by profile, to view all, new, subscribed, suggested, and un-subscribed communities, by industry and area of interest, by keywords, by publishing point (all publishing points, the local area network, the enterprise directory, and the global knowledge community directory), and by creation time (anytime, today, yesterday, this week, and last week). The semantic runtime client queries the publishing point endpoint listeners (for each publishing point) using the filters. It then gathers the results and displays them in the results pane. The user is also able to view the categories of each knowledge community in the results pane via a combo box. Figure 20B
illustrates the bottom portion of the Knowledge Communities Dialog Box.
I. CLIENT-SIDE SEMANTIC QUERY DOCUMENT SPECIFICATION FOR THE
INFORMATION NERVOUS SYSTEM
1. ~~a~garatgc query l~Iar~up Language (~QI~dL) ~vegvaevv In the currently preferred embodiment, the Nervana Semantic DHTML Behavior is an Internet Explorer DHTML Behavior that, from the client's perspective, every thing it understands as a query document. The client opens 'query documents,' in a manner resembling how a word processor opens 'textual and comlaound documents.' The l~Ter5,~ana client i~
primarily responsible for processing a Nervana semantic query document and rendering the results. A Nervana semantic query document is expressed and stored in form of the Nervana Semantic Query Markup Language (SQML). This is akin to a "semantic file format."
In the preferred embodiment, the SQML semantic file format comprises of the following:
~ Head - The 'head' tag, like in the case of HTML, includes tags that describe the document.
~ Title - The title of the document.
~ Comments - The comments of the document.
~ UserName - The username of the document creator.
~ SystemName - The systemname of the device on which the document was created.
~ Subject - The subject of the document.
~ Creator ~ The creator of the document.

~ Company - The company in which the document was created.
~ RequestType - This indicates the type of request. It can be "smart request"
(indicating requests to one or more information community web services) or "dumb request" (indicating requests to one or more local or network resources).
~ ObjectType - This fully qualifies the type of objects returned by the query.
~ URI - The location of the document.
~ CreationTime - The creation time of the document.
~ LastModifiedTime - The last modified time of the document.
~ LastAccessedTime - The last accessed time of the document.
~ Attributes - The attributes of the document, if any.
~ RevisionNumber - The revision number of the document.
~ Language - The language of the document.
~ Version - this indicates the version of the query. This allows the web service's semantic query processor to return results that are versioned. For instance, one version of the browser can use V 1 of a query, and another version can use V2.
This allows the web service to provide backwards compatibility both at the resource level (e.g., for agents) and at the link level.
~ Targets - This indicates the, names and the URLs of the information community web services that the query document targets.
~ Type - this indicates the type of targets. This can be "targetentries," in which case the tag includes sub-tags indicating the actual web seuvice targets, or "allsubscribedtargets," in which case the query processor uses all subscribed infomnation communities.
~ Categories - This indicates the list of category URLs that the query document refers to. Each "category" entry contains a name attribute and a URI attribute that indicates the URL of the Knowledge Domain Server (KDS) from which the category came.
o 'type - this indicates the type of categories. This can lae either "categoryentries,"
in which case the sub-tag refers to the list of category entries, "allcategories," in which case all categories are requested from the information community web services, or "myfavoritecategories," in which case the query processor gets the user's favorite categories and then generates compiled SQML that contains these categories (this compiled SQML is then sent to the server(s)).
~ Query - This is the parent tag for all the main query entries of the query document ~ Resource - The reference to the 'dumb' resource being queried. Examples include file paths, URLs, cache entry identifiers, etc. These will be mapped to actual resource managers components by the interpreter.
~ Type - The type of resource reference, qualified with the namespace.
Examples of defined resource reference types are: nervana:url (this indicates that the resource reference is a well-formed standard Internet URL, or a custom Nervana URL like 'agent://..."), nervana:filepath (this indicates that the resource reference is a path to a file or directory on the file-system), and nervana:namespaceref (this indicates that the resource comes from the client semantic namespace).

Uri - This indicates the universal resource identifier of the resource. In the case of paths and Internet URLs, this indicates the URL itself. In the case of namespace entries, this indicates the GUID identifier of the entry.
Mid - This indicates the metadata identifier, which is used by the SQML
interpreter to map the resource to the metadata section of the document. The metadata id is mapped to the same identifier within the metadata section.
~ Args - This indicates the arguments of the resource identifier.
~ Links - this indicates the reference to the semantic links (for "targets"
only) ~ Type - this indicates the type of links. This can be "linkentries,"
indicating the links are explicit entries.
~ LinkEntries - this indicates the details of a link entry.
~ Predicate - this indicates the type of predicate for the link. For instance, the predicate "nervana:relevantto" indicates that the query is "return all objects from the resource R that are relevant to the obj ect O," where R and O and the specified resource and object, respectively. Other examples of predicates include nervana:reportsto, nervanaaeammateof, nervana:from, nervanaao, nervana:cc, nervana:bcc, nervana:attachedto, nervanaaentby, nervanaaentto, nervana:postedon, nervana:containstext, etc.
~ Type - this indicates the type of object reference indicates in the 'Link' tag.
Examples include standard XML data types like xmlatring, xml:integer, Nervana equivalents of same, custom Nervana types like nervana:datetimeref (which could refer to object references like 'today' and 'tomorrow'), and any standard Internet URL (IiTTP, FTP, etc.) or Nervana URL (objects://, etc.) that refers to an object that Nervana can process as a semantic XML obj ect.
~ Metadata - this contains the references to the metadata entries.
~ MetadataEntry - this indicates the details of a metadata entry.
~ Mid - this indicates the metadata identifier (GUID).
o ~lalue - this indicates the nmtadata itself EX~IPLE: DOCUMENTS (INFORMA.TION OR CONTEXT-EASED) <?xml version--"1.0" encoding--"utf 8"?>
<sqml>
<head requesttype="smart request"
obj ecttyp e--"context\headlines"
uri="c:\foo's\bar.pd~' creationtime="foo"
lastmodifiedtime="foo"
lastaccessedtime="foo"
attributes="0"
revisionnumber="0"
language--"foo"
version--"foo" />
<title>foo</title>

<comments>foo</comments>
<username>foo</username>
<systemname>foo</systemname>
<subj ect>foo</subj ect>
<creator>foo</creator>
<company>foo</company>
<targets>
<target name--"Marketing"
reftype--"uri"
ref--"kisp://marketingldefault.wsdl"
/>
<target name--"Research"
reftype--"uri"
ref--"kisp://research/default.wsdl"
/>
</targets>
<categories>
<category name--"renters\pharmaceuticals\biotechnology"
reftype="uri"
ref--' 'kdsp ://renter s. com/categories.wsdl?id=4.5"
/>
<category name--"renters\pharmaceuticals\life sciences"
reftype="uri"
ref--"kdsp://renters.com/categories.wsdl?id=57"
/>
</categories>
/>
<resources>
<resource name--"foo"
type--"information\documents\general document"
reftype="nervana: filepath"
ref--"file://c:\bar.doc"
mid--"7886e4a0-S Sd9-45 ac-a084-97adc6fffdOf' ~.gs ""
/>
<resource name--"foo"
type--"information\all information"
reftype="nervana:url"
red"file://c:\bar.doc"
mid--"Olfc64a3-c068-4339-bc97-17e5ff37e93f' args-""
/>
<resource name--"foo"
type--"information\all information"
reftype="nervana:folderpath"
ref--"file://c:\"
mid--"f8cc39c3-e4fb-4a29-beta-d2faf36eb3a0"
args--"includesubfolders=true"
/>
<resource name--"foo"
type--"information\documents\general document"
reftype="nervana:url"
red"http://www.bar.com/doc.htm"
mid--"fbcc39c3-e4~-4a29-beta-d2faf36eb3 a0"
Wigs ""
/>
<resource name--"foo"
type--"information\documents\general document"
reftype="nervana:url"
ref--"ftp://gate.com/doc.txt"
mid="f8cc39c3-e4fU-4a29-beta-d2faf36eb3a0"
args ""
/>
<resource name="foo"
type=66information\docunmnts\general docurrmzlt"
reftype="nervana:filepath"
red"file://\\servers\server\file.pdf mid="1b870a25-4e98-45d8-a444-f0283a495357"
~.gs=""
/>
<resource name---"foo"
type--"information\documents\text document"
reftype="nervanaaext"
red""
mid="7886e4a0-55d9-45ac-a084-97adc6fffdOf args=""
/>
<resource name="foo"
type="information\documents\general document"
reftype="nervana: cacheentr~' red"ef~c90ea-282d-46d6-b355-ac8a4fc2f3e5"
mid--""
~.gs ""
/>
<resource name-"foo"
type="information\email\email message"
reftype="nervana:url"
red"request://email. all@ibm.com"
mid--""
args-""
/>
<resource name----"foo"
type="information\email\email annotation"
reftype--"nervana:url"
ref--"objects://rad.com/agency.asp"
mid--""
args=""
/>
<resource name--"foo"
type 6'infonnation\documents\general document"
reftype="nervana:url"
ref--"obj ects: //rad. conn/agency. asp"
mid=""
~.gs=""
/>
<resource name--"foo"
type--"information\documents\general document"
reftype="nervana:url"
red"obj ects ://rad. com/agency. asp"
mid="'' args=""
/>
<resource name--"foo"
type="information\documents\general document"
reftype="nervana:url"
red"request://docurnents.all@intel.com"
mid--""
~.gs ""
/>
</resources>
<links>

clink />
clink />
clink />
clink />
clink operator="and"
predicate="nervana:relatedto"
name--"foo"
type--"information\documents\general document"
reftype="nervana: filepath"
red"file://c:\foo.doc"
mid--"7886e4a0-SSd9-45ac-a084-97adc6fffd0~' args--""
operator="and"
predicate---"nervana: contains"
name--"foo"
type="information\documents\general document"
reftype"nervanaaext"
ref--""
mid="46ea76cb-1383-4885-af6f Oe0fc6a66896"
~,gs ~"' operator--"and'9 predicate=6'nervana:postedon"
name--' 'foo"
type="types\datetime"
reftype="nervana:datetimeref"
ref--""
mid="3fa64c3c-4754-4380-91b5-521299036c62"
~,gs-~,~' operator--"and"
predicate="nervana:relatedto"
name="foo"
type--"information\documents\general document"
reftype="nervana:url"
ref--"kisp://98@in.com/m.asp"
mid="c2649c39-alc3-4ca8-ae8d-c85c04372e9a"
args ""
operator="and"
predicate="nervana:isofpriority"
name--"foo"
type="types\priority"
reftype="nervana:priority"

red""
mid--"69bbc048-98c8-4f76-8edf Sa00ce91c183"
args--""
/>
</links>
<metadata>
<metadataentry mid--"7886e4a0-SSd9-45ac-a084-97adc6fffd0~' reftype--"uri"
ref--"file://c:\foo\bar.pdf ' />
<value>
<document>
<title>scenario modelling</title>
<type>text</type>
<format>application/pdf</format>
<filepath>c:\foo\bar.pdf</filepath>
<shortfilename>bar.pdf</shortfilename>
<creationtime>foo</creationtime>
<lastmodifiedtime>foo</lastmodifiedtime>
<lastaccessedtime>foo</lastaccessedtime>
<attributes>0</attributes>
<si~e>0</size>
<subj ect>foo</subj ect>
<creator>foo</creator>
<manager>foo</manager>
<company>foo</company>
<category>foo</category>
<keywords>foo</keywords>
<comnients>foo</comments>
<hlinkbase>foo</hlinkbase>
<template>foo</template>
<lastsavedby>foo</lastsavedby>
<revisionnumber>0</revisionnumber>
<totaleditingtime>foo</totaleditingtime>
<numpages>0</numpages>
<numparagraphs>0</numparagraphs>
<numlines>0</numlines>
<numwords>0</numwords>
<numcharacters>0</numcharacters>
<numcharacterswithspaces>0</niuncharacterswithspaces>
<numbytes>0</numbytes>
<language>foo</language>
<version>foo</version>
<abstract>foo</abstract>
</document>

</value>
/>
<metadataentry mid--"bfcb12b4-70bb-473a-847c-ebffe187828p' reftype="uri"
red"file://c:\foo\bar.pd~' />
<value>
<email>
<title>scenario modelling</title>
<type>text</type>
<format>applicatioi~/pdf</format>
<filepath>c:\foo\bar.pdf</filepath>
<shortfilename>bar.pdf</shortfilename>
<creationtime>foo</creationtime>
<lastmodifiedtime>foo</lastmodifiedtime>
<lastaccessedtime>foo</lastaccessedtime>
<attributes>0</attributes>
<size>0</size>
<subj ect>foo</subj ect>
<creator>foo</creator>
<manager>foo</manager>
<company>foo</company>
<category>foo</category>
<keywords>foo</keywords>
<comments>foo</comments>
<hlinkbase>foo</hlinkbase>
<template>foo</template>
<lastsavedby>foo</lastsavedby>
<re~risionnumber>0</re~isionnumber>
<totaleditingtime>foo</totaleditingtime>
<numpages>0</numpages>
<numparagraphs>0</numparagraphs>
<numlines>0</numlines>
<numwords>0</numwords>
<numcharacters>0</numcharacters>
<numcharacterswithspaces>0</numcharacterswithspaces>
<numbytes>0</numbytes>
<language>foo</language>
<version>foo</version>
<abstract>foo</abstract>
</email>
</value>
/>
</metadata>
</sqml>

2. SQML Generation Preferably, SQML is generated in any one or more of several possible ways:
~ By creating a smart request ~ By creating a local request ~ By creating an entity ~ By opening one or more local documents in the semantic browser ~ By the client (dynamically) - in response to a drag and drop, smart copy and paste, intrinsic alert, context panel/link invocation, etc.
3. SQML Parsing In some embodiments in some situations, SQML that gets created on the client might not be ready (in real-time) for remote consumption - by the server's XML web service or at another machine site. This is especially likely to be the case when the SQML refers to local context such as documents, Entities, or Smart Requests (that are identified by unique identifiers in the semantic environment).1 In the preferred embodiment, the client generally creates SQML that is ready for remote consumption. Preferably, it does this by caching the metadata for all references in the metadata section of the document. This is preferable because in some cases, the resource or object to which the reference points might no longer exist when the query is invoked. For instance, a user might drag and drop a document from the W ternet to a smart request in order to generate a nevi relational request. The client extracts the metadata (including the summary from the link and inserts the metadata into the SQML. Because the resolution of the query uses only the me~adata, the query is ready for consumption once the metadata is inserted into the SQML
document. However, the link that the object refers to might not exist the day after the user found it. In such a case, even if the user invokes the relational request after the link might have ceased to exist, the request will still work because the metadata would already have been cached in the SQML.
The client SQML parser performs "lazy" updating of metadata in the SQML. When the request is invoked, it attempts to update the metadata of all parameters (resources, etc.) in the SQML to handle the case where the objects might have changed since they were used to create I Blenders (or collections) contain references to smart requests.

the relational request. If the object does not exist, the client uses the metadata it already has.
Otherwise, it updates it and uses the updated metadata. That way, even if the obj ect has been deleted, the user experience is not interrupted until the user actually tries to open the object from whence the metadata came.
J. SEMANTIC CLIENT-SIDE RUNTIME CONTROL API SPECIFICATION FOR
THE INFORMATION NERVOUS SYSTEM
1. Introducing the Nervana Semantic Runtime Control - Overview In the preferred embodiment, the Nervana Semantic Runtime Control is an ActiveX
control that exposes properties and methods for use in displaying semantic data using the Nervana semantic user experience. The control will be primarily called from XSLT skins that take XML data (using the SRML schema) and generate DHTML+TIME or SVG output, consistent with the requirements of the Nervana semantic user experience.
Essentially, in this embodiment, the Nervana control encapsulates the "SDI" on top of which the XSLT slcins sit in order to produce a semantic content-driven user experience. The APIs listed below illustrate the functionality that will be exposed or made available by the final API set in the preferred embodiment.
The l~crvana ~c~aantic ~untimc control API
a. EnumObjectslnl~amesgacePath INTRODUCTION
The EnumObjectslnNamespacePath method returns the objects in a namespace path.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to open a namespace path in order for the user to navigate the namespace from within the semantic browser.
PROTOTYPE
SCODE
EnumObj ectsInNamespacePath( [in] BSTR Path, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );
b. CompileSemanticQueryFromBuffer INTRODUCTION
The CompileSemanticQueryFromBuffer method opens an SQML buffer and compiles it into one or more execution-ready SQML buffers. For instance, an SQML file containing a blender will be compiled into SQML buffers representing each blender entry. If the blender contains blenders, the blenders will be unwrapped and an SQML buffer will be returned for each contained blender. A compiled or "execution-ready" SQML buffer is one that can be semantically processed by an agency. The implication is that a blender that has agents from multiple agencies will have its SQML compiled to buffers with the appropriate SQML from each agency.
Note: If the buffer is already compiled, the method returns S FALSE and the return arguments are ignored.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to compile an SQI~IIL buffer- and retrieve generated "compiled code" that is ready for execution. In typical scenarios, the application or skin will compile an SQML buffer and then prepare frame windows where it wants each individual SQML query to sit. It can then issue individual SQML semantic calls by calling OpenSemanticQueryFromBuffer and then have the results displayed in the individual frames.
PROTOTYPE
SCODE
CorripileS emanticQueryFromBuffer( [in] BSTR SQMLBuffer, [in] DWORD Flags, [out] DWORD *pdwNumCompiledBuffers, [out] BSTR *pbstrCompiledBuffers );

c. OpenSemanticQueryFromBuffer INTRODUCTION
The OpenSemanticQueryFromBuffer method opens an SQML buffer and asynchronously fires the XML results (in SRML) onto the DOM, from whence a Nervana skin can sink the event. Note that in this embodiment the SQML has to be "compiled" and ready for execution. If the SQML is not ready for execution, the call will fail. To compile an SQML
buffer, call CompileSemanticQueryFromBuffer.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to open a compiled SQML buffer.
PROTOTYPE
SCODE
OpenSemanticQueryFromBuffer( [in] BSTR SQMLBuffer, [in] DWORD Flags, [out] GUS *pQuerylD );
d. GetSemanticQueryBufferFromFile IhTTR~DUC'B"I~h~T
The GetSemanticQueryBufferF°romF'ile method opens an SQML file, and returns the buffer contents. The buffer can then be compiled and/or opened.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to convert an SQML file into a buffer before processing it.
PROTOTYPE
SCODE
GetSemanticQueryBufferFromFile [in] BSTR SQMLFiIePath, [in] DWORD FileOpenFlags, [out] BSTR *pbstrSQMLBuffer );

e. GetSemanticQuerySufferFromNamespace INTRODUCTION
The GetSemanticQueryBufferFromNamespace method opens a namespace object, and retrieves its SQML buffer.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to open an SQML buffer when it already has access to the id and path of the namespace object.
PROTOTYPE
SCODE
Gets emanticQueryBufferFromNamespace( [in] GU>D Objector, [in] BSTR Path, [out] BSTR *pbstrSQMLBuffer );
f c~et~en~antic~ueryBufferFromUl~L
INTRODUCTION
The GetSemanticQueryBufferFromURL method wraps the URL in an SQML buffer, and returns the buffer.
USAGE ~CEI'~lAl~1~
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to convert an URL of any type to SQML. This can include file paths, HTTP
URLs, FTP URLs, Nervana agency object URLs (prefixed by "wsobject://") or Nervana agency URLs (prefixed by "wsagency://").
PROTOTYPE
SCODE
Gets emanticQueryBufferFromURL( [in] BSTR URL, [out] BSTR *pBuffer );

g. GetSemanticQueryBufferFromClipboard INTRODUCTION
The GetSemanticQueryBufferFromClipboard method converts the clipboard contents to SQML, and returns the buffer.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to get a semantic query from the clipboard. The application can then load the query buffer.
PROTOTYPE
SCODE GetSemanticQueryBufferFromClipboard( [out] BSTR *pBuffer );
h. Stop INTRODUCTION
The Stop method stops current open request.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to stop a load request is just issued.
PROTOT'~ PE
SCODE Stop( [in] GUS Query );
i. Refresh INTRODUCTION
The Refresh method refreshes the current open request.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will call this method to refresh the currently loaded request.
PROTOTYPE
SCODE Refresh( [in] GU1D Querym );

j. CreateNamespaceObject INTRODUCTION
The CreateNamespaceObject method creates a namespace object and returns its GUm.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will typically call this method to create a temporary namespace object when a new query document has been opened.
PROTOTYPE

SCODE

CreateNamespaceObj ect( [in]BSTR Name, [in]BSTR Description, [in]BSTR QueryBuffer, [in]LONG AgentObjectType, [in]LONG Attributes, [in]LONG NamespaceObjectType, [out]GUS ~p~bject~ );

k. DeleteNamespaceObject INTRODUCTION
The DeleteNmnespaccObje~,t method deletes a namespac~ objee,t.
USAGE ~CE1~TAP~1(~
A Nervana client application (for instance, the semantic browser) or a Nervana skin will typically call this method to delete a temporary namespace object.
PROTOTYPE
SCODE DeleteNamespaceObject( [in] GUm Objector );
1. CopyObject INTRODUCTION
The CopyObject method copies the semantic object to the clipboard as an SQML
buffer using a proprietary SQML clipboard format. The object can then be "pasted"
onto agents for relational semantic queries, or used as a lens over other objects or agents.

USAGE SCENARIO
A Nervana skin will typically call the CopyObject method when the user clicks on the "Copy" menu option - off a popup menu on the object.
PROTOTYPE
SCORE CopyObject( [in] BSTR ObjectSRML );
m. CanObjectBeAnnotated INTRODUCTION
The CanObjectBeAnn.otated method checks whether the given object can be annotated.
USAGE SCENARIO
A Nervana skin will typically call the CanObjectBeAnnotated method to determine whether to show UI indicating the "Annotate" command.
PROTOTYPE
SCORE CanObjectBeAimotated( [in] BSTR bstrObjectSRML );
n. AnnotatcObjcct INTRODUCTION
The AnnotateObj ect method involves the currently installed email client and initializes it tea send ~n mnail annotation of the object to the ernail agent caf the ~.gency frown ~rhence the object came.
USAGE SCENARIO
A Nervana skin will typically call the AnnotateObject method when the user clicks on the "Annotate" menu option - off a popup menu on the obj ect.
PROTOTYPE
SCORE AnnotateObject( [in] BSTR bstrObjectSRML );
o. CanObjectBePublished INTRODUCTION
The CanObjectBePublished method checks whether the given object can be published.

USAGE SCENARIO
A Nervana skin will typically call the CanObjectBePublished method to determine whether to show UI indicating the "Publish" command.
PROTOTYPE
SCODE CanObjectBePublished ( [in] BSTR bstrObjectSRML );
p. PublishObject INTRODUCTION
The PublishObj ect method invokes the currently installed email client and initializes it to send an email publication of the object to the email agent of the agency from whence the object came.
USAGE SCENARIO
A Nervana skin will typically call the PublishObject method when the user clicks on the "Publish" menu option - off a popup menu on the obj ect.
PROTOTYPE
SCODE AnnotateObject( [in] BSTR bstrObjectSRML );
q. OpenObjectContents I~.TTR~DUCTI~1~T
The OpenObjectContents method opens the object using an appropriate viewer.
For instance, an email object will be opened in the email client, a document will be opened in the browser, etc..
USAGE SCENARIO
A Nervana skin will typically call the OpenObjectContents method when the user clicks on the "Open" menu option - off a popup menu on the object.
PROTOTYPE
SCODE OpenObjectContents ( [in] BSTR ObjectSRML );
los r. SendEmailToPersonObject INTRODUCTION
The SendEmailToObject method is called to send email to a person or customer object.
The method opens the email client and initializes it with the email address of the person or customer obj ect.
USAGE SCENARIO
A Nervana skin will typically call the SendEmailToObject method when the user clicks on the "Send Email" menu option - off a popup menu on a person or customer obj ect.
PROTOTYPE
SCODE SendEmailToObject( [in] BSTR ObjectSRML );
s. GetObjectAnnotations INTRODUCTION
The GetObjectAnnotations method is called to get the annotations an object has on the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the GetObjectAnnotations method when it wants to display the titles of the annotations an object has - for instance, in a popup menu or when it wants to display the annotations metadata in a window.
PROTOTYPE
SCODE
GetObj ectAnnotations( [in] BSTR ObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );
t. IsObjectMarkedAsFavorite INTRODUCTION
The IsObj ectMarkedAsFavorite method is called to check whether an obj ect is marked as a favorite on the agency from whence it came.

USAGE SCENARIO
A Nervana skin will typically call the IsObjectMarkedAsFavorite method to determine what UI to show - either the "Mark as Favorite" or the "Unmark as Favorite"
command. If the object cannot be marked as a favorite (for instance, if it did not originate on an agency), the error code E INVALIDARG is returned.
PROTOTYPE
SCODE
IsObjectMarkedAsFavorite( in] BSTR ObjectSRML );
u. MarkObjectAsFavorite INTRODUCTION
The MarkObjectAsFavorite method is called to mark the object as a favorite on the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the MarkObj ectAsFavorite method when the user clicks on the "Mark as Favorite" command.
PROTOTYPE
SCODE
MarkAsFavorite( in] BSTR ObjectSRML );
v. IJa~~aark~bjectA~Favoritc INTRODUCTION
The UmnarkObj ectAsFavorite method is called to urunark the obj ect as a favorite on the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the UnmarkObjectAsFavorite method when the user clicks on the "Umnark as Favorite" command.

PROTOTYPE
SCODE
UnmarkAsFavorite( in] BSTR ObjectSRML );
w. IsSmartAgentOnClipboard INTRODUCTION
The IsSmartAgentOnClipboard method is called to check whether a smart agent has been copied to the clipboard.
USAGE SCENARIO
A Nervana skin will typically call the IsSmartAgentOnClipbOard method when it wants to toggle the user interface to display the "Paste" icon or when the "Paste"
command is involved.
PROTOTYPE
SCODE
IsSmartAgentOnClipboard();
x. GetSmartLensQueryBuffer Il'~T'1CI~~DUC~1CI~I'~T
The GetSmartLensQueryBuffer method is called to get the query buffer of the smart lens.
This returns the SQML of the query that represents the objects on the smart agent that is on the clipboard, and which are semantically relevant to a given object.
IJ~AGE ~CEI'~T1~
A Nervana skin will typically call the GetSmartLensQueryBuffer method when the user hits "Paste as Smart Lens" to invoke the smart lens off the smart agent that is on the clipboard.
PROTOTYPE
SCODE
GetSmartLensQueryBuffer( [in] BSTR ObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );

y. OpenObjectContents INTRODUCTION
The OpenObjectContents method opens the object using an appropriate viewer.
For instance, an email object will be opened in the email client, a document will be opened in the browser, etc.
USAGE SCENARIO
A Nervana skin will typically call the OpenObjectContents method when the user clicks on the "Open" menu option - off a popup menu on the object.
PROTOTYPE
SCODE OpenObjectContents( [in] BSTR ObjectSRML );
Part 3. Email Control APIs a. lEmail Get~'romlainlg~bjects II~TRODUCTYOh~
The Email GetFromLinkObjects method is called to get the metadata for the "From"
links on an email object from the agency from whence it carne.
~J~~W'~ ~cWI~A~~1~
A Nervana skin will typically call the Email GetFromLinkObj acts method when it wants to navigate to the "From" list from an email object, or to display a popup menu with the name of the person in the "From" list.
PROTOTYPE
SCODE
Email_GetFromLinkObj acts( [in] BSTR EmailObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );

b. Email GetToLinkObjects INTRODUCTION
The Email GetFromLinkObj ects method is called to get the metadata for the "To" links on an email object from the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the Email GetToLinkObjects method when it wants to navigate to the "To" list from an email object, or to display a popup menu with the name of the person in the "To" list.
PROTOTYPE
SCODE
Email_GetToLinkObj ects( .
[in] BSTR EmailObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );
c. Email_Get~''LcILinl~~bjects INTRODUCTION
The Email GetCcLinkObjects method is called to get the metadata for the "CC"
links on an entail object from the agency from whence it came.
U~~GE ~(~EI'~TA L~If A Nervana skin will typically call the Email GetCcLinlcObjects method when it wants to navigate to the "CC" list from an email object, or to display a popup menu with the name of the person in the "CC" list.
PROTOTYPE
SCODE
Email_GetCcLinkObj ects( [in] BSTR EmailObjectSRML, [in] LONG QueryMask, a [out] BSTR *pQueryRequestGuid );

d. Email GetBccLinkObjects INTRODUCTION
The Email GetBccLinkObjects method is called to get the metadata for the "BCC"
links on an email obj ect from the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the Email GetBccLinkObj ects method when it wants to navigate to the "BCC" list from an email object, or to display a popup menu with the name of the person in the "BCC" list.
PROTOTYPE
SCODE
Email GetBccLinkObjects( [in] BSTR EmailObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );
e. Email GetAttaclaanentlLinl~~bject~
INTRODUCTION
The Email GetAttachmentLinkObjects method is called to get the metadata for the "Attachment" links on an email obj ect from the ~.gency from whence it came.
TIJ~AGE ~~",lEI'~TAI~lf~
A Nervana skin will typically call the Email GetAttachmentLinkObjects method when it wants to navigate to the "Attachments" link from an email object, or to display a popup menu with the titles of the attachments in the "Attachments" list.
PROTOTYPE
SCODE
Email_GetAttachmentLinkObj ects( [in] BSTR EmailObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );

4. Person Control APIs a. Person GetDirectReports INTRODUCTION
The Person GetDirectReports method is called to get the metadata for the "Direct Reports" links on a person object from the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the Person GetDirectReports method when it wants to navigate to the "Direct Reports" link from a person object, or to display a popup menu with the names of the direct reports in the "Direct Reports" list.
PROTOTYPE
SCODE
Person_GetDirectReports( [in] BSTR EmailObjectSRML, [in] LONG QueryMask, [out] BSTR ~'pQueryRequestCauid );
b. Person GetDistributionLists INTRODUCTION
The Person GetDistributionLists method is called to get the metadata for the "Member of Distributi~n Lists"' links on a person object from the agency from whence it came.
USAGE SCEI~ARI~
A Nervana skin will typically call the Person GetDistributionL,ists method when it wants to navigate to the "Member of Distribution Lists" link from a person object, or to display a popup menu with the names of the distribution lists of which the person is a member.
PROTOTYPE
SCODE
Person_GetDistributionLists( [in] BSTR PersonObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );

c. Person GetInfoAuthored INTRODUCTION
The Person GetInfoAuthored method is called to get the metadata for the "Info Authored by Person" links on a person object from the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the Person GetInfoAuthored method when it wants to navigate to the "Info Authored by Person" link from a person obj ect, or to display a preview window with time-critical or recent information that the person authored.
PROTOTYPE
SCODE
Person_GetInfoAuthored( [in] BSTR PersonObjectSRML, [in] BOOL SemanticQuery, [in] LONG QueryMask, [out] BSTR °'°pQueryRequestGuid );
d. Person GetInfoAnnotated INTRODUCTION
The Person GetInfoAnnotated method is called to get the metadata for the "Info Annotated lay Person" links on a person object from tlhe agency from whence it came.
USAGE SCEl~TARI~
A Nervana skin will typically call the Person GetInfoAnnotated method when it wants to navigate to the "Info Annotated by Person" link from a person object, or to display a preview window with time-critical or recent information that the person annotated.
PROTOTYPE
SCODE
Person_GetInfoAnnotated( [in] BSTR PersonObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );

e. Person GetAnnotationsPosted INTRODUCTION
The Person GetAnnotationsPosted method is called to get the metadata for the "Annotations Posted by Person" links on a person object from the agency from whence it came.
USAGE SCENARIO
A Nervana skin will typically call the Person GetAnnotationsPosted method when it wants to navigate to the "Annotations Posted by Person" link from a person object, or to display a preview window with time-critical or recent annotations that the person posted.
PROTOTYPE
SCODE
Person_GetAnnotationsPosted( [in] BSTR PersonObjectSRML, [in] LONG QueryMask, [out] BSTR *pQueryRequestGuid );
f l~er~oa~ ~endlEx~aaafll'o INTRODUCTION
The Person SendEmailTo method is called to send email to a person or customer object.
The method opens the email client and initialises it with the email address of the person or customer obj ect.
USAGE SCENARIO
A Nervana skin will typically call the Person SendEmailTo method when the user clicks on the "Send Email" menu option - off a popup menu on a person or customer object.
PROTOTYPE
SCODE Person SendEmailTo( [in] BSTR ObjectSRML );

5. System Control Events a. Event:OnBeforeQuery INTRODUCTION
The OnBeforeQuery event is fired before the control issues a query to resources consistent with the current semantic request.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will sink this event if it wants to cancel a query or cache state before the query is issued.
PROTOTYPE

VOm OnB
eforeQuery( [in] GUID Querym, [in] BSTR QueryBuffer, [in] DWORD QueryMask, [in] DWORD Flags, [out]BOOL Cancel );

b. Event:OnQueryBegin INTRODUCTION
The OnQueryBegin event is fired when the control issues the first query to a resource consistent with the current semantic request.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana slcin will sink this event if it wants to cache state or display status information when the query is in progress.
PROTOTYPE
VOID
OnQueryBegin( [in] GUm Objector );
c. Event:OnQueryComplete INTRODUCTION
The OnQueryComplete event is fired before the control issues a query to resources consistent with the current semantic request.

USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will sink this event if it wants to cancel a query or cache state before the query is issued.
PROTOTYPE
VOID
OnQueryComplete( [in] GUID QuerylD );
d. Event:OnQueryResultsAvailable INTRODUCTION
The OnQueryResultsAvailable event is fired when there are available results of an asynchronous method call. The event indicates the request GUID, via which the caller can uniquely identify the specific method call that generated the response.
USAGE SCENARIO
A Nervana client application (for instance, the semantic browser) or a Nervana skin will sink this event to get responses to method calls on the control.
PROTOTYPE

VOID

OnQueryResultsAvailable( [in] GUS QueryID, [in] SCODE QueryResult, [in] ESTR Results, [in] DWORD NumResults, [in] DWORD QueryMask, [in] VARIANT ResultsParam );

e. Appendix A
QUERY MASK VALUES
#define QM RESULTS 0x01 #define QM RESULTCOUNT 0x02 #define QM NEWRESULTS 0x04 #define QM NEWRESULTCOUNT 0x08 #define QM DEFAULT ( QM RESULTS ) Example:
Person GetInfoAuthored( PersonObj ectSRML, QM_RESULTS ~ QM_RESULTCOUNT, &QueryRequestGuid );
K. SECURITY SPECIFICATION FOR THE INFORMATION NERVOUS SYSTEM
1. Authorization INTRODUCTION
The 'People' DSA will be initialized with an LDAP Directory URL and Group Name.
The 'Users' DSA will also be initialized with an LDAP Directory URL and Group Name.
Typically, the 'Users' will be a subset of 'People.' For instance, a pharmaceuticals corporation might install a KIS for different large pharmaceutical categories (e.g., Biotechnology, Life Sciences, Pharmacology, etc). Each of these will have a group of users that are knowledgeable or interested in that category. However, the KIS will also have the 'People' group populated with all employees of the corporation. This will enable users of the KIS to navigate to members of the entire employee population even though those members are not users of the ISIS. W
addition, the inference engine will be able to infer expertise with semantic links off people that are in the corporation, not necessarily just users of the KIS.
This is also advantageous for access control at the KIS level - this complements or supplements access control provided by the application serer at the W eb service layer. The Users group will contain people that have access to the KIS knowledge.
However, the People group will contain people that are relevant to the KIS knowledge, even though those people don't have access to the KIS.
Both People and Users DSA populate the People table in the Semantic Metadata Store (SMS) and indicate the object type id appropriately. Note that preferably the passwords are NOT
stored in the People table in the SMS.
The Users DSA also populates the User Authentication Table (UAT). This is an in-memory hash table that maps the user names to passwords. The server's Web service will implement the IPasswordProvider interface or an equivalent. The implementation of the PasswordProvider object will return the password that maps to a particular user name. The C#
example below illustrates this:
namespace WSDK_Security f public class PasswordProvider : Microsoft.WSDK.Security.IpasswordProvider f public string GetPassword( string username ) f return "opensezme";
The following C# code shows how the Web service can retrieve the user information after the user has been authenticated:
using System;
using System.Collections;
using System.ComponentModel;
using System.Data;
using System.Diagnostics;
using System.Web;
using System.Web.Services;
using Microsoft.WSDK.Security;
using Microsoft.WSDK;
namespace WSDK Security public class Seuvicel : System.Web.Ser~%ices.WebSer~iice f [WebMethod]
public string PersonalHello() f string response = "";
SoapContext requestContext = HttpSoapContext.RequestContext;
if (requestContext == null) f throw new ApplicationException("Non-SOAP request.");
foreach (SecurityToken tok in requestContext.Security.Tokens) f if (tok is UsernameToken) f response +_ "Hello " + ((UsernameToken)tok).Username;
) ) ) ) return response;
The Nervana Web service can then go ahead and call the Server Semantic Runtime with the calling user name. The runtime then maps this to SQL and uses the appropriate filters to issue the semantic query.
For the Nervana ASP.NET application, the following entry is added as a child of the parent configuration element in the Web.config file:
<microsoft.wsdk>
<security>
<p asswordProvider type="WSDK_Security.PasswordProvider, WSDK-Security" />
</security>
</microsoft.wsdk>
a. Client-Side Authorization Request In order to create a Userna~neTolcen for the request, the Nervana client has to pass the username and password as part of the SOAP request. The Nervana client can pass multiple tokens as part of the request - this is preferable for cases where the user's identity is federated across multiple authentication providers. The Nervana client will gather all the user account information the user has supplied (including user name and password information), convert these to WS-Security tokens, and then issue the S~AP request. The client code will look like the following (reference: http:llwww.msdn.microsoft.com):
localhost.Servicel proxy = new localhost.Servicel();
UseniameToken clearTextToken = new UsernameToken("Joe", "opensezme", PasswordOption.SendHashed);
proxy.RequestSoapContext.Security.Tokens.Add(clearTextToken);
labell .Text = proxy.PersonalHello();
b. Validating the UsernameToken on the Server (http://msdn.microsoft.com/libraryldefault.asp?url=llibrary/en-us/dnwssecur/htmllwssecwithwsdk. asp) Although the WSDK verifies the Security header syntax and checks the password hash against the password from the Password Provider, there is some extra verification that is preferably be performed on the request. For instance, the WSDK will not call the Password Provider if a UsernameToken is received that does not include a password element. If there is no password to check, there is no reason to call the password provider. This means we need to verify the format of the UsernameToken ourselves.
Another possibility is that there is more than one UsernameToken element included with the request. WS-Security provides support for including any number of tokens with a request that may be used for different purposes.
The code above can be modified for the Nervana Web method to verify that the UsernameToken includes a hashed password and to only accept incoming requests with a single UsernameToken. The modified code is listed below.
[WebMethod]
public string ProcessSemanticQuery( string Query ) SoapContext requestContext = HttpSoapContext.RequestContext;
if (requestContext == null) f f throw new ApplicationException("Non-SOAP request.");

d if (requestContext.Security.Tokens.Count == 1) foreach (SecurityToken tolc in requestContext.Security.Tokens) f if (tok is UsernameToken) f UsernameToken UserToken = (UsernameToken)tok;
if (UserToken.PasswordOption = PasswordOption.SendHashed) ( return ProcessSemanticQueryInternal( Query, UserToken.Username );
else f throw new SoapException( "Invalid UsernameToken password type.", SoapException.ClientFaultCode);

else throw new SoapException( "UsernameToken security token required.", SoapException.ClientFaultCode);
) else f throw new SoapException( "Request must have exactly one security token.", SoapException.ClientFaultCode);
return null;
2. People Groups The KIS will include metadata for people groups. These are not unlike user groups in modem operating systems. The People Group will be a Neuvana first-class object (i.e., it will inherit from the ~bject class). In addition, the People Group schema will be as follows:
Field Name Field Type Description ~bject~ String The object id of the people group 1'Tame String 'I'he name of the people roup DescriptionSti-in The descri tion of the eople group URI String The URL of the people group -this uniquely identifies the group and in the preferred embodiment, will be an LDAP URT

In most cases, people groups will map to user groups in directory systems (like LDAP) For instance, the KIS server admin will have the KIS crawl a configurable set of user groups.
There will be a People DSA that will crawl the user groups and populate the People Groups and Users tables in the SMS. The People DSA will perform the following actions:
~ Create the group (if it doesn't exist in the SMS) or update the metadata of the Group (if it exists).
~ Enumerate all the users in the group (at the source - an LDAP directory in the preferred embodiment).

~ For all the users in the group, create People objects (or update the metadata if the objects already exist in the SMS).
~ Update the semantic network (via the 'SemanticLinks' table in the ' SMS) by mapping the people objects to the group objects (using the BELONGS TO GROUP
semantic link type). This ensures that the SMS has semantic links that capture group membership information (in addition to the groups and users themselves).
3. Identity Metadata Federation Identity Metadata Federation (IMF) refers to a feature wherein an Information Community (agency) is deployed over the Internet but is used to service corporate or personal customers. For instance, Reuters could set up an information community for all its corporate customers that depend on its proprietary content. In such a case where multiple customers share an information community (likely in the same industry), Reuters will. have a group on the SMS
for each customer. However, each of these customers would have to have its corporate directory mirrored on Reuters in order for people metadata to be available. This would cause problems, particularly from a security and privacy standpoint. Corporations will probably not be comfortable with having external content providers obtaining access to the metadata of their employees. 1MF addresses this problem by having the Internet-hosted information community (agency) host only enough metadata for authentication of the user. For instance, Reuters will store only the logon information fear the users of its corporate custorraers, in its SM a. WImr2 the semantic browser receives SRML containing such incomplete metadata, the client will then issue another query to the enterprise directory (via LDAP access or via UDDI if the enterprise directory metadata is made available through a Web services directory) to fetch the complete metadata of the user. This is possible because the externally stored metadata will have the identity information with which the remaining metadata can be fetched. Since the client fetches the remaining metadata within the firewall of the enterprise, the sensitive corporate metadata is not shared with the outside world.

4. Access Control a. Access Control Policy In the preferred embodiment, the ISIS will include and enforce access control semantics.
The KIS employs a policy of "default access." Default access here means that the ISIS will grant access to the calling user to any metadata in the SMS, except in cases where access is denied. As such, the system can be extended to provide new forms of denial, as opposed to new forms of access. In addition, this implies that if there is no basis for denial, the user is granted access (this leads to a simpler and cleaner access control model).
The KIS will have an Access Control Manager (ACM). The ACM is primary responsible for generating a Denial Semantic Query (DSQ) which the SQP will append to its query for a given semantic request from the client. The ACM will expose the following method (C#
sample):
String GetDenialSemanticQuery( String CallingUserName Preferably, the method tales in the calling user name and returns a SQL query (or equivalent) that encapsulates exception objects. These are objects that must not be returned to the calling user by the SQP (i.e., objects for which the user does not have access).
The SQP then builds a final raw query that includes the denial query a~
follo~v~:
Aggregate Raw Query AND NOT IN (Denial Query) For example, if the aggregate raw query is:
SELECT OBJECTm FROM OBJECTS WHERE OBJECTTYPE~ = 5, and the denial query is:
SELECT OBJECTm FROM OBJECTS WHERE OWNERUSERNAME <>
'JOHNDOE', The final raw query (which is that the SQP will finally execute and serialize to SRML to return to the calling user) will be:
SELECT OBJECTm FROM OBJECTS WHERE OBJECTTYPEm = 5 AND NOT 1N

(SELECT OBJECTID FROM OBJECTS WHERE OWNERUSERNAME <>
'JOHNDOE') Semantically, this is probably equivalent to:
"Select all objects that have an object type id of 5 but that are not in an object list not owned by John Doe."
This in turn is probably semantically equivalent to:
"Select all objects that have an object type id of 5 that are owned by John Doe."
b. General Access Control Rules Each semantic query processed by the semantic query processor (SQP) will contain an access control check. This will guarantee that the calling user only receives metadata that he/she has access to. The SQP will employ the following access control rules when processing a semantic query:
1. Preferably, if the query is for 'People' objects (people, users, customers, experts, newsmakers, etc.), the returned 'People' objects must either:
~ hzclude the calling user, or ~ Include people that share at least one people group with the calling user, and be owned by the calling user or the system Preferably, the corresponding denial query maps to the following rule: The returned objects must satisfy the following:
~ Is not the calling user +
~ Is not owned by the calling user or the system +
~ Has people that do not share any people group with the calling user Sample Denial Query SQL
The SQL below illustrates the access control denial query that will be generated by the ACM and appended by the SQP to enforce the access control policy. In this example, the name of the calling user is 'JOHNDOE.' SELECT OBJECTID FROM OBJECTS WHERE
OWNERUSERNAME <> 'JOHNDOE' OR
OWNERUSERNAME <> 'SYSTEM' OR
WHERE OBJECTID NOT IN (SELECT OBJECTID FROM PEOPLE WHERE
NAME='JOHNDOE') OR
WHERE OBJECTID NOT IN
(SELECT OBJECTID FROM SEMANTICLINKS WHERE
OBJECTTYPEID - ' 'PERSON AND
PREDICATETYPEID='BELONGS TO GROUP' AND SUBJECTID IN (SELECT
SUBJECTID FROM SEMANTICLINKS WHERE OBJECTID 1N (SELECT OBJECTID
FROM PEOPLE WHERE NAME='JOHNDOE' ) ) 2. Preferably, if the query is for non-People objects (documents, email, events, etc.), the returned objects must:
~ Be owned by the calling user or the system user, and ~ Be the subject of a semantic link with the calling user as the object, or ~ Be the object of a semantic link with the calling user as the subject, or ~ Be the subject of a semantic link with the object being a person that shares at least one people group with the calling user, or ~ Be the object of a semantic link with the subject being a person that shares at least one people group with the calling user Preferably, the corresponding denial query maps to the following rule: The returned objects must satisfy the following:
~ Is not owned by the calling user +
Is not o~~Jned by the system user +
Is not the subject of a semantic link with the calling user as the object +
Is not the object of a semantic link with the calling user as the subject +
~ Is not the subject of a semantic link with the object being a person that shares at least one people group with the calling user +
~ Is not the object of a semantic link with the subject being a person that shares at least one people group with the calling user Safnple Denial Query SQL
The SQL below illustrates the access control denial query that will be generated by the ACM and appended by the SQP to enforce the access control policy. In this example, the name of the calling user is 'JOHNDOE.' SELECT OBJECTID FROM OBJECTS WHERE OWNERUSERNAME <>
'JOHNDOE' OR
OWNERUSERNAME <> 'SYSTEM' OR
OBJECTID NOT IN (SELECT OBJECTID FROM SEMANTICLINKS WHERE
its OBJECTTYPEID = "PERSON' AND OBJECTID IN (SELECT OBJECTID FROM
PEOPLE WHERE NAME='JOHNDOE') OR
WHERE OBJECTID NOT IN (SELECT OBJECTID FROM SEMANTICLINI~S
INNER JOIN PEOPLE WHERE SEMANTICLINKS.SUBJECTTYPEID='PERSON' AND
SEMANTICLINKS.SUBJECTm = PEOPLE.OBJECTID) OR
OBJECTID NOT IN (SELECT OBJECTID FROM SEMANTICLINI~S WHERE
OBJECTTYPEID='PERSON' AND PREDICATETYPEID='BELONGS TO GROUP' AND
SUBJECTID IN (SELECT SUBJECTID FROM SEMANTICLINI~S WHERE OBJECTID IN
(SELECT OBJECTID FROM PEOPLE WHERE NAME='JOHNDOE')) OR
OBJECTID NOT IN (SELECT OBJECTID FROM SEMANTICLINKS WHERE
OBJECTTYPEID='PERSON' AND PREDICATETYPEID='BELONGS TO GROUP' AND
OBJECTID IN (SELECT OBJECTID FROM PEOPLE WHERE NAME='JOHNDOE')) Sample Mef ged Denial Quefy SQL
By merging these two rules, the ACM returns the following merged query to the SQP for access denial:
SELECT OBJECTID FROM OBJECTS WHERE
OWNERUSERNAME <> 'JOHNDOE' OR
OWNERUSERNAME <> 'SYSTEM' OR
OBJECTS NOT IN (SELECT OBJECTS FROM PEOPLE WHERE
NAME='JOHNDOE') OR
OBJECTS NOT 1N (SELECT OBJECTS FROM SEMANTICLINI~S WHERE
OBJECTTYPEID - ' 'PERS ON AND
PREDICATETYPEID='BELONGS TO_GROUP' AND SUBJECTID IN (SELECT
SUBJECTID FROM SEMANTICLINKS WHERE OBJECTID IN (SELECT OBJECTID
FROM PEOPLE WHERE NAME='JOHNDOE')) OR
OBJECTID NOT I1~J (SELECT OBJECTII~ FRO1~11 SEMA1~1TI~'LINhS i~HERE
OBJECTTYPE~ _ ° 'PERSON' AND OBJECTS IN (SELECT OBJECTS FROM
PEOPLE
WHERE NAME='JOHNDOE') OR
OBJECTID NOT IN (SELECT OBJECTS FROM SEMANTICLINI~S INNER JOIN
PEOPLE ON SEMANTICLINKS.SUBJECTTYPEID='PERSON' AND
SEMANTICLINI~S.SUBJECT~ = PEOPLE.OBJECTID) OR
OBJECTID NOT IN (SELECT OBJECTS FROM SEMANTICL1NKS WHERE
OBJECTTYPEID='PERSON' AND PREDICATETYPE117='BELONGS TO GROUP' AND
SUBJECTID IN (SELECT SUBJECTID FROM SEMANTICL1NKS WHERE OBJECTID IN
(SELECT OBJECTID FROM PEOPLE WHERE NAME='JOHNDOE')) OR
OBJECTID NOT IN (SELECT OBJECTID FROM SEMANTICLINKS WHERE
OBJECTTYPEID='PERSON' AND PREDICATETYPEID='BELONGS TO GROUP' AND
OBJECTID IN (SELECT OBJECTID FROM PEOPLE WHERE NAME='JOHNDOE')) Example Scenario For instance, A Reuters agency (KIS) might have people groups for each enterprise customer that Reuters serves. The agency will have a common information base (Reuters content) but will have people groups per enterprise customer. These groups might include competitors. As such, it is preferable to ensure that the knowledge flow, generation, and inference do not cross competitor boundaries. For instance, an employee of Firm A must not derive knowledge directly from an employee of Firm B that competes with Firm A, not must he or she derive knowledge indirectly (via inference). An employee of Finn A must not be able to get recommendations for items annotated by employees of Firm B. Or an employee of Firm A
must not be able to find experts that work for Firm B. Of course, this assumes that Firm A and Firm B are not partners in some fashion (in which case, they might want to share knowledge). In the case of knowledge partners, Reuters would create a people group (likely via LDAP) that includes the people groups of Firm A and Firm B. The Reuters KIS will then have the following people groups: Firm A, Firm B, and Finns A&B. The SMS will also include metadata that indicates that the people in Firms A and Firms B belong to these groups (via the "belongs to gro~p99 semantic link type). 5~ith this process in place, the aforementioned rules will guarantee that knowledge gets shared between Firms A and B.
c. Access Control Rules for Annotations In the case of annotations, the calling user will be editing the semantic network, as opposed to querying it. W this case, the following rules would apply:
1. Preferably, if the obj ect being annotated is a Person obj ect, the obj ect must either be:
~ The calling user, or ~ A person that shares at least one people group with the calling user, and be owned by the calling user or the system 2. Preferably, if the object being annotated is a non-Person object (e.g., a document, email, event, etc.), the object must either be:
~ Owned by the calling user ~ Owned by the system Sample Denial Query SQL
The SQL below illustrates the access control denial quexy that will be generated by the ACM (for checking access control for annotations) and appended by the SQP to enfoxce the access control policy. In this example, the name of the calling user is 'JOHNDOE.' SELECT OBJECTID FROM OBJECTS WHERE
OWNERUSERNAME ~ 'JOHNDOE' OR
OWNERUSERNAME ~ 'SYSTEM' OR
OBJECTID NOT IN (SELECT OBJECT117 FROM PEOPLE WHERE
NAME='JOHNDOE') OR
OBJECTID NOT IN (SELECT OBJECTID FROM SEMANTICLINKS WHERE
OBJECTTYPEID='PERSON' AND PREDICATETYPE117='BELONGS TO GROUP' AND
OBJECTID 1N (SELECT OBJECTID FROM SEMANTICLINKS WHERE OBJECTID IN
(SELECT OBJECTID FROM PEOPLE WHERE NAME='JOHNDOE')) Access Cofztf°ol EsZfoYCemer~t The ACM enforces access control fox annotations and other write operations on the KIS.
The KIS XML Web Service exposes an annotation method as follows (C# sample):
Annotate~bject( string CallingUserName, String ObjectID );
This method calls the ACM to get the denial query. It then creates a final query as follows:
Annotation ~bject Query .l~ll~ 1~IO'T hl (Denial Query) In the preferred embodiment, the annotation object query is always of the form:
SELECT OBJECTII? FROM OBJECTS WHERE OBJECTID=ObjectlD, where ObjectlD is the argument to the AnnotateObject method.
The ACM then builds a final access control query SQL and uses this SQL to check for access control. Because the ACM does not have to return the SQL, it merely invokes it directly in order to check for access control. In addition, because it is a binaxy check (access or no access), the ACM merely checks whether the 'denial query returns at least one row. For instance, a final query might look like:
SELECT OBJECTID FROM OBJECTS WHERE OBJECTID = ObjectlD AND NOT IN
(SELECT OBJECTm FROM OBJECTS WHERE OWNERUSERNAME ~ 'JOHNDOE') The ACM then runs this query (via the SQL query processor) and asks for the count of the number of rows in the result set. If there is one row, access is granted, else access is denied.
Tlus model is implemented this way in order to have consistency with the denial query model (the ACM always builds a denial query and uses this as a basis for all access control checks).
L. DEEP INFORMATION SPECIFICATION FOR THE INFORMATION
NERVOUS SYSTEM
Deep Information Overview INTRODUCTION
In the preferred embodiment, the Nervana 'Deep Info' tool is aimed at providing context-sensitive story-like information for a Nervana information object. Deep Info essentially provides Nervana users with information that otherwise would be lost, given a particular context. By way of rough analogy, Deep Info is like the contextual information that gets displayed on music videos on MTV (showing information on the current artist, the current song, and in some case, the current musical instrument in the song).
The 'deep' in 'deep info' refers to the fact that the contextual information will often span multiple "hops" in the semantic network on the agency from whence the object came. 'Deep Info9 is comprised of 'deep inf~ nuggets' which caai either be plan textual mEtadata or metadata with semantic query links (via SQML).
In the preferred embodiment, there are at least five kinds of Deep Info nuggets:
1. Basic Semantic Linlc Nuggets 2. Context Template Nuggets 3. Trivia Nuggets 4. Matchmaker Nuggets 5. Recursive Nuggets a. Basic Semantic Link Nuggets With basic semantic link truths, deep info nuggets merely convey a semantic link of the current object. These nuggets involve a semantic link distance of 1. In this case, there is overlap with what will be displayed in the 'Links' context/task pane. Examples are:

~ Patrick Schmitz reports to Nosa Omoigui ~ Patrick Schmitz has 5 Direct Reports ~ Patrick Schmitz annotated 47 objects ~ Patrick Schmitz authored 13 objects ~ Patrick Schmitz was copied on 56 email objects b. Context Template Nuggets Context template nuggets display contextual information for each relevant context template, based on the information at hand. These nuggets are identical to those that will be displayed in the context bar or context panel for each type of context template. For example:
~ Patrick Schmitz posted 3 breaking news items ~ Patrick Schmitz posted 14 classics ~ Patrick Schmitz authored 7 headlines ~ Patrick Schmitz is involved in 13 discussions ~ Patrick Schmitz is a newsmaker on 356 objects c. Trivia Nuggets For all email objects on an agency:
Steve Judkins appears on the "To" list of all of them ~ Steve Judkins replied to 23% of them ~ Patrick Schmitz annotated 50% of them ~ Only 3 of these have a thread depth greater than 2 For all people objects on an agency:
Patrick Schmitz has sent email to 4~7% of them 14.% of them report to Nosa Omoigui ~ Sally Smith has had discussions with~85% of them ~ 12% of them are newsmakers on at least one topic ~ All of them have been involved in at least one discussion this week ~ 33% of them are experts on at least one topic ~ 8% of them are experts on more than three topics For a given distribution list on an agency:
~ Steven Judkins has posted the most email to this list ~ Sarah Trent has replied to the most email on this list ~ Nosa Omoigui has never posted to this list ~ Patrick Schmitz has posted 87 messages to this list this month ~ Richard Novotny has posted 345 messages to this list this year For all distribution lists on an agency:
~ Steven Judkins has posted the most email to all lists ~ Lisa Heibron has replied to email on only 2% of the lists ~ Nosa Omoigui has never posted to any list ~ Patrick Schmitz has posted at least once every week to all the lists ~ Richard Novotny has posted messages on 3 lists For all information objects on an agency:
~ Steven Judkins has been the most prolific publisher (he published 5% of them) ~ Sally Smith has been the most prolific annotator (she annotated 2% of them) ~ Nosa Omoigui has been the most active newsmaker ~ Patrick Sclnnitz has the most aggregate expertise ~ Steve Judkins has the most expertise for information published this year ~ Gavin Schmitz has been involved in the most discussions (12% of them) ~ Richard Novotny has been involved in the most discussions this month (18% of them) d. Matchmaker Nuggets Pcrson To Pcrson Semantic Rink Based ~ Patrick Schmitz has sent mail to 13 people ~ 47 people have appeared on same To list as Patrick Schmitz ~ 47 people have appeared on same CC list as Patrick Schmitz 89 people in total have been referenced on email sent by Patrick Schmitz 24. people have annotated the same information as Patrick Schmitz 3 people are on all the same distribution lists as Patrick Schmitz ~ 29 people are on at least one of Patrick Schmitz's distribution lists Context-Tem~alate Based 12 people have expertise on the same information categories as Patrick Schmitz 14 people and Patrick Schmitz are newsmakers on the same information items 27 people are in discussions with Patrick Schmitz Information To Person Semantic Link Based ~ Patrick Schmitz posted this information item ~ Steve Judkins authored this information item ~ This information item was copied to 2 people ~ 3 people annotated this information item Context Template Based (similar to context template nuggets) one ~ There are 4 experts on this information item ~ There are 27 newsmakers on this information item Information To Information Context Template Based (similar to context template nuggets) ~ There are 578 relevant 'all bets' ~ There are 235 relevant 'best bets' ~ There are 4 relevant breaking news items ~ There are 46 relevant headlines Semantic Link Based (via people) ~ There are 21 information items that have the same experts with this one ~ There are 23 information items that have the same newsmakers with this one ~ There are 34 information items posted by the same person that posted this one ~ There are 34 information items authored by the same person that authored this ~ There are 44 information items annotated by people that annotated this one e. Recuar~ive l~~Ta~gget~
With recursive nuggets, displaying deep info on the subject of the current information nugget forms a contextual hierarchy. The system then recursively displays the nuggets based on the object type of the subject. With recursive nuggets9 the system essentially probes the semantic network starting from the source object and continues to display nuggets along the path of the network. Frobing is preferably stopped at a depth that is consistent with resource limitations and based on user feedbaclc.
Another way to think of recursive nuggets is like a contextual version of an business organization chart. However, with Deep Information in the Information Nervous System, users will be able to browse a tree of KNOWLEDGE, as opposed to a tree of INFORMATION. To talce an example, if a user selects an object and a tree view will show up like what is displayed below:
Example with document as context:
[+]Newsmakers on 'Title of document' [+] Gavin Schmitz [+] Reports To ->

[+] Steve Judkins [+] Experts Like Steve Judkins ->
[+] Nosa Omoigui [+] Patrick Schmitz [+] Interest Group Like Steve Judkins ->
[+] Patrick Schmitz [+] Chuck Johnson [+]Direct Reports ->
[+]Joe Williams [+] Direct Reports D
[+] Interest Group Like Joe Williams ->
[+] Richard Novotny [+] Nosa Omoigui [+] Interest Group [+] Experts Example with email as context:
[+] Emai1 is From:
[+] Nosa Omoigui [+] Experts like Nosa Omoigui [+] Email is To:
[+] ~'huck. Johnson [+] Experts like Chuck Johnson [+] Email is Copied To:
[+] Richard Novotny [+] Experts like Richard Novotny [+] Email Attachments:
foo.doc [+] Experts on foo.doc [+] Gavin Schmitz [+] Newsmakers like Gavin Schmitz [+] Newsmakers on 'Title of Email' Example with conversation object as context:

[+]Conversation Participants [+]Steve Judkins [+] Interest Group Like Steve Judkins...
[+]Nosa Omoigui [+] Interest Group Like Nosa Omoigui [+] Experts on 'Title of Conversation' [+] Richard Novotny [+] Interest Group Like Richard Novotiiy Notice the use of default predicates in the above example - e.g., with People subjects linked to People objects, the LIKE predicate is uses (e.g., Interest Group LIKE Richard Novotny).
Another example of recursive nuggets is shown below:
[+] Patrick Sclnnitz authored this email [+] Patrick Schmitz reports to Nosa Omoigui [+] Nosa Omoigui has 6 Direct Reports [+] Steve Judkins . ..
[+] Steve Judkins posted ...
[+] Steve Judkins is an expert on ...
[+] Steve Judkins is a newsmaker on . . . ' [+] Steve Judkins has been involved in 6 discussions [etc.]
[+] Richard Novotny. . .
[+] [The remaining ti direct reports]
[+] Nosa Omoigui annotated 13 objects...
[+] [More context template nuggets on the 13 obj acts]
[+] Nosa Omoigui has authored 278 objects [+] Nosa Omoigui has annotated 23 items [...]
[+] Patrick Sclunitz has 5 Direct Reports [+] John Doe ...
[+] More Native Nuggets based on the direct reports [+] Patrick Schmitz annotated 47 objects In the preferred embodiment, recursive nuggets will most typically be displayed via a drill-down pane beside each result object in the semantic browser. This will allow the user to select a result object and then recursively and semantically "explore" the object (as illustrated above).

Also, each header item in the Deep Info drill down tree view will be a link to a request (e.g., Experts Like Steve Judkins), and each result will be a link to an entity. For example, users will be able to "navigate" to the "person" (semantically) Patrick Schmitz from anywhere in the Deep Info tree view. Users will then be able to view a dossier on Patrick Schmitz, copy Patrick Schmitz, and Paste it on, say, Breaking News - in order to open a request called Breaking News by Patrick Schmitz. Again, notice the use of a default predicate based on the Person subject ("By°°), The preferred embodiment Presenter Deep Info tree view (with support from the semantic runtime API in the semantic browser) will keep track of those links that are requests and those links that are result objects; that way, it will intelligently interpret the user's intent when the user clicks on a link the tree view (it will navigate to a request or navigate to an entity).
M. CREATE REQUEST WIZARD SPECIFICATION FOR THE INFORMATION
1~TER~~1LT~ ~~~'lI°°EI'~JII
Introducing the Create Request Wizard OVERVIEW
The preferred embodiment Create Request (or Smart Agent) Wizard allows the user to easily and intuitively create new requests that represent semantic queries t~
be issued to one ~r more knowledge sources (running the IW owledge Integration Service).
Wizard Page 1: Select a Profile and Request Type: This page allows the user to select what profile the request is to be created in. The page also allows the user to select the type of request he/she wants to create. This type could be a Dossier (Guide) which will create a request containing sub-requests for each context template (based on the filters indicated in the request), knowledge types (corresponding to context templates such as Best Bets, Headlines, Experts, Newsmakers, etc.), information types (corresponding to types such as Presentations, General Documents, etc.), and request collections which are Blenders and allow the user to view several requests as a cohesive unit. See Figure 17A.

Wizard Page 2: Select Knowledge Communities (Agencies): This page allows the user to select which knowledge communities (running on Knowledge Integration Servers (KISes) the request should get its knowledge from. The user can indicate that the request should use the same knowledge communities as those configured in the selected profile. The user can alternatively select specific knowledge communities. See Figure 17B.
Wizard Page 3: Select Filters: This page allows the user to select which filters to include in the request. Filters can include one or more of the following: keywords, text, categories, local documents, Web documents, email addresses (for People filters), and Entities.
In alternate embodiments, other filter types will be supported. The property page also allows the user to select the predicate with wluch to apply a specific filter. Preferably, the most cormnon predicate that will be exposed is "Relevant to." Other predicates can be exposed consistent with the filter type (for instance a filter that refers to a Person via an email address or entity will use the default predicate 6'B~" if the requested type is not 'People' - e.g. Headlines B~ John Smith and will use the default predicate "LIKE" if the request type is 'People' - e.g., Experts LIKE John Smith).
The property page also allows the user to select the operation with which to apply the filters.
The two most common operators are AND (in which case only results that satisfy all the filters are returned) and OR (in which case results that satisfy any of the filters are returned). See Figure 17C.
Wizard Page 4: Name and describe this request: This page allows the user to enter a name and description for the request. The wizard automatically suggests a name and description for the request based on the semantics of the request. Examples include:
1. Headlines on Security AND on Application Development AND on Web Services.
2. Experts from R&D on Encryption Techniques~OR on User Interface Design, etc.
3. Presentations on Artificial Intelligence.
4. Dossier on Data Mining AND on Web Development. See Figure 17D.
The user is allowed to overnde the suggested name/description. The suggestions are truncated as needed based on a maximum name and description length.

The semantic browser also exposes the properties of an existing request via a property sheet. This allows the user to "edit" a request. The property sheet exposes the same user interface as the wizard except that the fields are initialized based on the semantics of the request (by de-serializing the request's SQML representation). See Figure 17E.
N. CREATE PROFILE WIZARD SPECIFICATION FOR THE INFORMATION
NERVOUS SYSTEM
Introducing the Create Profile Wizard OVERVIEW
The Create Profile Wizard allows the user to easily and intuitively create new user profiles.
Wizard Page 1: Select your areas of interest: This page allows the user to select his/her areas of interest. This allows the semantic browser to get some high-level information about the user's knowledge interests (such as the industry he/she works in). This information is then used to nanow category selections in the categories dialog, recommend new knowledge communities (agencies) configured with knowledge domains consistent with the user's areas) of interests, etc.
See Figure 45A.
W izard Page 2: Select your kno~Jledge communities: This page alloy's the user to subscribe to knowledge cormnunities for the profile. This allows the semantic browser to "know" wluch knowledge sources to issue requests to, when those requests are created for the profile. The semantic browser also uses the knowledge communities in the profile when it invokes Visualizations, semantic alerts, the smart lens (when the lens is a request/agent for the given profile), the obj ect lens (when the target obj ect is a result from the given profile), when the user drags and drops (or copies and pastes) an object to a request/agent for the given profile, etc.
See Figure 45B.
Wizard Page 3: Nasne and describe this profile: This page allows the user to enter a name and description for the profile. The page also allows the user to indicate whether the profile is preferably made the default profile. 'The default profile is used when the user does not explicitly indicate a profile in any operation in the semantic browser (for example, dragging and dropping a document from the file system to the icon representing the semantic browser will open a bookmark with that document from the default profile, whereas dragging and dropping a document to an icon representing a specific profile will open a bookmark with that profile). See Figure 45C.
O. CREATE BOOKMARK WIZARD SPECIFICATION FOR THE INFORMATION
NERVOUS SYSTEM
1. Introducing the Create Bookmark Wizard OVERVIEW
The Create Bookmark (or Local/Dumb Request Agent) Wizard allows the user to easily and intuitively create new bookmarks (local/dumb requests) to view Ioca1/Web documents, entities, etc. in the semantic browser via which he/she can get access to the toolbox of the system (i.e., drag and drop, smart copy and paste, smart lens, smart alerts, Visualizations, ete.).
Wizard Page 1: Select a Profile and Request Type: This page allows the user to select what profile the bookmark is to be created in. The page also allows the user to add/remove items to/from the bookmark. See Figure 46A.
Wizard Page 2: Name and describe this bookmark: This page allows the user to enter a name and description for the bookmark. The wizard automatically suggests a name and description for the bookmark based on the items in the bookmark. Examples include:
~ Document 1, Document 2, and Document 3 ~ Documents Matching 'Encryption' ~ Documents in the Folder 'My Documents' and Subfolders ~ Nervana Presentation (July 2003).ppt AND Documents Matching "Security" in the Folder 'My Documents' and Subfolders The user is allowed to override the suggested name/description. The suggestions are truncated as needed based on a maximum name and description length. See Figure 46B.

2. Scenarios Show me all Presentations on Protein Engineering Using the Create Request Wizard, select the Presentations information-type (in Documents~Presentations), and then select the Protein Engineering category as a filter. Hit Next - the wizard intelligently suggests a name for the request (Presentations on Protein Engineering) based on the semantics of the request. The wizard also selects the right default predicates. Hit Finish. The wizard compiles the query, sends the SQML to the KISes in the selected profile, and then displays the results.
3. Intelligent Publishing-Tool Metadata Suggestion and Maintenance While the Information Nervous System does not rely or depend on metadata that is stored by Publishing Tools (e.g., the author of a document), having such metadata available and reliable can be advantageous. One problem with prior art is that publishing tools (e.g., Microsoft Word, Adobe Acrobat, etc.) do not intelligently manage the metadata creation and maintenance process.
Here are some ways that the preferred embodiment of the present invention can be used to make the metadata creation and maintenance process better:
a. When the user creates a new document, add the author's email address (this can be programmatically retrie~red from the useus entail client and in the event that the user has several addresses, the publishing tool should prompt the user for which address to use) to the metadata header of the document (rather than merely the author's name). This is because email addresses provide much more uniqueness (for instance, the name 'John Smith' could refer to one of millions of people - as such the existence of such data in the metadata of a document is not that useful). Note that one possible email address to use in the metadata header can be retrieved from, say, the logged on user's single sign-on account (e.g., Microsoft PassportTM).
b. When the doctunent is edited and if the current user is different from the author of the document (as is indicated in the metadata header), prompt the user if helshe wants to change the metadata header accordingly. This provides some basis form of intelligent metadata maintenance.

This model can be applied across different object types and metadata fields in cases where the publishing tool can validate the field (e.g., as in the case of the currently logged on user's name and email address).
P. SEMANTIC THREADS SPECIFICATION FOR THE INFORMATION
NERVOUS SYSTEMTM
1. Semantic Threads OVERVIEW
In the preferred embodiment, semantic threads are objects in the KIS semantic network that represent threads of 'annotations or conversations. They are different from regular email threads in that they are also semantic - they have object identifiers and type identifiers (the OBJECTTYPEID THREAD identifier) thread-specific semantic links, they convey meaning via one or more ontology-based knowledge domains and they support dynamic linking.
Also, because they are first-class objects in the Itafonmation I~Tervous system, they coal be queried, copied, pasted, dragged, dropped, and used with the smart and object lenses.
Figure 23 illustrates a semantic thread object and its semantic links.
Because a semantic thread object is a first-class member of the semantic network and the entire Information l~Tervous System, it is subject to manipulation, presentation and querying like other obj ects in the system. For example, the semantic browser will allow the user to navigate from a Person object to all threads that that person has participated in (via the "Participant"
predicate - with a predicate type id of PREDICATETYPEm PARTIGIPANTOFTHREAD).
The user can then navigate from the thread to all the thread's participants (People) and keep dynamically navigating from then on. To take another example, a thread object can also be a Best Bet in a given context (or none, if none is specified).
In the preferred embodiment, the semantic thread object also conveys meaning.
This is advantageous because it means that the thread can be returned via a semantic query in the system. For instance, "Find me all threads on Topic A and Topic B." The KIS
maintains semantic links for semantic threads just like it does with other objects such as documents.

However, because semantic threads can refer to multiple objects, the semantics of the thread evolve with the obj ects the thread contains. For example, a thread can start with one topic and quickly evolve to include other topics. Email threads can end in a very different "semantic domain" from where they started - participants introduce new perspectives, new information is added to the thread, email attachments might be added to the thread, etc., all on the basis of meaning.
The KIS manages the "semantic evolution" of semantic threads. It does this by adding semantic links to the thread to "track" the contents of the thread. For instance, if a thread starts off with one document and an annotation, the KIS adds a semantic liuc to the thread for each to which the category the document and annotation belong. In other words, the thread is asserted to have the same semantics as the document and annotation it contains. If another annotation is added to the thread (e.g., if a user annotates the first annotation), the ISIS
computes a new link strength for the categories of the new annotation that are already linked off the thread. It is preferable if it does this because the new annotation can attenuate or strengthen the semantics of the entire thread from a particular perspective. However, this modification of the strength of the semantic links) for the categories that are already present off the thread are preferably done on a per-category basis - as with other objects, the thread can belong to multiple categories with different strengths. The new link strength can be computed in at least two ways: in a simple embodiment, the average of all link strengths for the category being linked to the thread is used.
However, this has the disadvantage that too many items in the thread of weak strength can erode the "perceived" (as far as the ISIS semantic query processor is concerned) semantics of the entire thread. An alternative embodiment is to use the maximum link strength.
However, this also has a disadvantage that the semantics of the thread might remain fixed to a domain/category even though the thread "has moved on" to new domains/categories. From a weighted-average perspective, this would likely return confusing results as the thread grows in size.
W the preferred embodiment, the KIS preferably computes a weighted average of all the link strengths for the categories to be linked to the thread. This new weighted average becomes the link strength. The weighted average is preferably computed using the number of concepts in each object in the thread. This has the benefit of ensuring that "semantically light" objects (such as short postings) do not erode the semantics of the thread relative to "semantically denser"
objects in the thread (such as email attachments and long postings). The number of concepts, and not the size, is preferably used in the preferred embodiment because the size of the object is a less reliable indicator of the conceptual weight of the object. For instance, a document could contain images or could include much information that does not map well to key phrases or concepts Preferably, the computed weight could also include the time when the entry was added (thereby "aging" the semantics of older items relative to newer ones). This weight is then multiplied by the category link strength and the multiples axe added and then divided by the number of entries. Other weighting schemes can also be applied.
The following rules are applied when a new item is added to the semantic network and which is to be added to a semantic thread:
1. Categorize the new item to be added to the thread 2. For each category in the returned list of categories which are already on the semantic thread Compute new weighted-average link strength ~ Update category semantic link off the semantic thread object 3. For each category in the returned list of categories which are not already on the semantic thread f ~ Assign link strength ~ Add category semantic link off the semantic thread object The weighted-average link strength is computed as follows:
New Link Strength = ~ Ci * Li N

Where Ci is the normalized number of concepts (from 0 to 1) of object i, Li is the link strength of object i, and N is the number of objects in the thread (including the new object). The normalized number of concepts is computed by dividing the number of concepts in each object (extracted by the Knowledge Domain Manager (KDM)) by the number of concepts in the largest object in the thread (including the new object).
If a semantic thread comprises of standard, intrinsic (and unedited) email threads, the KIS
modifies the semantic network differently. This is because most email clients include all prior email messages that form the thread in the most recent email message. As such, in this case, the KIS preferably simply uses the most recent email message as being representative of the entire thread. To accomplish tlus, the KIS preferably categorizes the most recent email message, and replace all prior semantic links (relating to categories) from the thread object with new semantic links corresponding with the new categories and link strengths.
For non-email threads (for example, threads that form based on an annotation of an existing object in the semantic network), the model described above should be employed.
Alternatively, the KIS can maintain an Aggregate Thread Document (ATD) which is then categorized. This document should contain the text of the objects in the thread - roughly analogous to how an email message contains the text ofprior messages in the same thread.
When a new object is added to the thread, the KIS preferably updates the last-modified-time of the thread object in the Semantic Metadata Store (SMS).
2. Semantic Thread Conversations Semantic thread conversations in the Information Nervous System are a special form of semantic threads. Essentially, a conversation is a semantic thread that has more than one participant. Semantic thread conversations have the object type id, OBJECTTYPEll~ THREADCONVERSATION.

The KIS creates a thread based on the number of participants in that thread and could immediately create the thread as a thread conversation. Alternatively, the KIS
could "upgrade" a thread to a conversation once additional participants are detected.
3. Semantic Thread Management The pseudo-code below illustrates how the KIS adds preferred threads and conversations to the semantic networlc:
1. If an individual email message is detected and is a member of an existing thread obj ect f ~ Add the new email obj ect to the thread and update the semantic network ~ If the thread has more than one participant, change the thread's object type identifier to OBJECTTYPEID THREADCONVERSATION
2. If am email thread is detected f ~ Create a new thread object and update the semantic network ~ If the thread has more than one participant, change the thread's object type identifier to OBJECTTYPE~ THREI~CON~ERSATION
i 3. If an email amiotation of an existing obj ect is detected f ~ Add the annotation to the semantic network ~ If the annotated object is not itself an annotation ~ Create a new thread object and update the semantic network Else ~ Add the new annotation to the thread containing the annotated object (i.e., the existing annotation) and update the semantic network ~ If the updated thread has more than one participant, change the thread's object type identifier to OBJECTTYPEID THREADCONVERSATION

Q. SAMPLE SCREEN SHOTS
Figures 24-44B are additional screen shots further illustrating the functions, options and operations discussed above.
R. SPECIFICATION FOR SEMANTIC QUERY DEFINITIONS &
VISUALIZATIONS FOR THE INFORMATION NERVOUS SYSTEM
1. Semantic Images & Motion a. Overview Semantic images and motion can be an advantageous component of the preferred embodiment in terms of the Nervana semantic user experience. In other words, the user's experience with the system can be enhanced in an embodiment that has semantic image/motion metadata stored on a Nervana agency (information community) and accessed via the Nervana XML Web Service. Tn that embodiment, via Nervana, end users will have context and time-sensitive semantic access to their images. Imagine, for example only, using a Getty finages (or Corbis) agent as a smart lens over an email message - when invoked, this will open images that are semantically related to the message. Cr, imagine dragging and dropping a document from your hard drive to a Getty agent to view semantically related images. This will involve having image metadata (consistent with an image schema). The Nervana toolbox remains the same - we merely add a new information object type for images. Also, there are semantic, skins fox semantic images - different views, thumbnails, slide shows, filtering, aggregation, etc. For examples of semantic images, visit:
http://creative.gettyimages.cornlsource/search/resultsmain.asp?source=advSearch &hdnSy nc=Medicine%7E0%2C12%2C449%2C3%2C15%2C1%2C0%2C0%2C0%2C12287%2C0%2C
7%2C14%2C6%2C3%2C3%2C0%2C12%2C449%2Cen%2Dus&UQR=t~fwz Very generally, the properties of the semantic visualizations will vary depending upon several different variables. Among these variables will often be the context, including the context of what feature or property of the system is being invoked. In the next several sections some of the contextual variables that influence the semantic determinations will be listed and/or described. In many instances, there will be overlap or commonality of the variables or determinants of the semantic visualizations, but in some cases, the considerations or combination of considerations will be unique to the particular situation.
b. Industry-Specific Semantic Images and Motion Industry-specific semantic images/motion are images/motion that can be used (and in the preferred embodiment axe used) as part of the presentation atmosphere for semantic results for one or more categories (that map to industries). For instance, visit http://www.corbis.com and http://www.gettyimages.com and enter a search for the keywords listed below (which, in the aggregate, map to target industries, based on industry-standard taxonomies).
Such images/motion can also be used as backgrounds, filter effects, transformations, and animations for context and category skins (that map to context templates and categories).
In addition, these images/motion can be used for visuals for motion paths extracted from some of these images for superior screensavers. For example, imagine a skin displaying metadata and visualizations along a motion path extracted from one of these semantic images (e.g., metadata rotating inside a light bulb - for the "electric utilities9' industry), along with chrome with other surrounding unages and animations, etc. Other industries, with industry specific images and motion might include:
Pharmaceuticals Telecommunications Airlines Medicine Telecom Equipment Retail Healthcare Telecom Services Fashion Life Sciences Telecom Technology Advertising Biotechnology Telecom Regulations Aerospace Oil and gas Tobacco Defense Chemical Automotive A 'business Energy Automobiles Agriculture Electric Utilities Insurance Beverages Gas Utilities Consulting Business services Water Utilities Information E-commerce Technology Entertainment Technology Food Environmental Computer Equipment Forest products Services Publishing Computer Health Care Providers Manufacturers Real Estate Com utin Hos itality Financial Semiconductors Internet Brokerages Nanoteclmology Law Financial Services Public Sector Legal Banking Government Manufacturing Consumer Homeland Security Marketing Consumer Products Travel Media Consumer Services Tourism Networkin Communications Transportation For example, if the user launches a request/agent, Headlines on Bioinformatics or on Protein Engineering, the semantic browser will map the biotechnology-related categories from the SQML to a set of images in the biotechnology industry. It will then display one or more images as part of the skin for the results of the requestlagent (thereby proving a pleasant user experience as well as visually conveying the "mood" of the request/agent).
Figure 101 as a sample semantic image for Pharmaceuticals/Biotech industry (artistic DNA helix superimposed over a human face on the left and a organic chemical chart on the right, licensed from the Corbis web site).
The same applies to information types and context templates. Skins will do the smart thing based on the context/information type and the category/ontology and mix and match semantic imageslmotion across these properties in an intelligent manner. For instance, an agent titled "Headlines on Wireless Technology" can have chrome (and/or a smart hourglass - see below) that shows an image/motion-based animation toggling between a "Headlines"
imagelmotion and a "Wireless" image/motion. A blender titled "Headlines on Wireless and Breaking News on Semiconductors and Email by anyone in my group related to the product specification" can have chrome (and/or a smart hourglass) that "toggles"
between imageslmotion for "Headlines " "News " "Wireless " "Semiconductors " and "Email."
> > > >
iso The Presenter's query processor can enumerate all context template and information types and all categories (from the agent/blender SQML) and set up the chrome animation accordingly.
For information types, enter searches (e.g., on Corbis and Getty) for:
Documents Online Learning Email People Books Users Ma azines Customers Multimedia Also, for context templates, enter searches for:
Headlines Favorites _ News Places Discovery Time (for "timeline" and "upcoming events") Conversations Schedule o Ex erts o Ap ointment Also, note that semantic images/motion are preferably not completely random.
However, preferably they are not from a bounded set either. Preferably, they are carefully picked and then skins can randomly select fr~rr the chosen set. »ut, preferably they ire not randorr~ from the entire set on, for example, Corbis or Getty Tmages. Otherwise there may be silly images, cartoons, and some potentially offensive or inappropriate images. Also, some of these guidelines preferably vary depending on whether the skin theme is in subtle, moderate, exciting, or super-exciting mode. In subtle mode, the skin might decide to choose one image/motion per visualization pivot. In other modes, this would likely lead to a boring user experience.
In low-flashiness mode, the skin can use a semantic image/motion as part of the chrome -not unlike a PowerPoint slide-deck background (e.g., alpha blended). Semantic images/motion can also be used in the smart hourglass (see below) as well as in part of the visualization (on the context bar, panel, or palette). For visualizing context and information types, semantic images/motion are preferably carefully picked to clearly indicate the information type or context.
In addition, the selection mode can also be a skin property.
Also, the number of possible semantic images/motion used per skin would likely need to be capped - depending on where the images/motion are being displayed. However, in some scenarios, this might not be necessary. For instance, a blender skin might cycle between chrome backgrounds as the user navigates the blender results (from page to page or agent to agent) - to be consistent with what is currently being displayed from the blender. This can also be a skin property.
c. The Client-Side Semantic Image & Motion Cache The Presenter has a smart expandable client-side cache with semantic images and motions that are downloaded and stored on the client (on installation). Skins can then select from these pre-cached images and motions. The images/motions can be pre-cached based on the user's favorite categories and areas of interest (which he or she selects) -which map to target industries. Skins can then complement the pre-cached semantic images/motions with on-demand image queries to an image server (an XML Web Service that exposes server-side images/motions - hosted by I~Tervana or a third party like Corbis or C~etty Images).
'The Presenter will also do the smart thing and have a bias function such that recently downloaded images/motions are selected before older ones (as a tiebreaker). A
"usage count" is also cached along with each image/motion - the Presenter uses this count in filtering which images/motions to display and when. Such "load balancing" will yield a fresher and non-repetitive user experience.
The cache is preferably populated on demand (based on the user's semantic queries) - for instance, there is no point in pre-caching pharmaceutical images/motions for a user's machine at Boeing. Preferably, he cache size is also capped and the image cache manager preferably purges "old" and "unused" images using an LRU algorithm or the equivalent. This way, the cache can be in "semantic sync" with the user's agent usage pattern and favorite agent's list.

2. The Smart Hourglass A majority of the calls that the Nervana Presenter will make to provide the "semantic user experience" probably will be remote calls to the XML Web Service. As such, there will be unpredictable, potentially unbounded delays in the UI. One can expect a fair amount of bandwidth and server horsepower within the enterprise but the Nervana user interface must still "plan" for unknown latency in method invocations.
Operating systems today have this problem with unbounded I/O calls to disk or to the network. Some CPU-bound operations also have substantial delays. In the Windows and Mac UI, the user is made to perceive delay via a "wait" cursor - usually in the shape of an "hourglass."
In the preferred embodiment, the Presenter will have semantic hints (via direct access to the SQML "method call") with which it can display the equivalent of a "smart or semantic hourglass." This could be in the form of an intermedi~.te page that displays "Loading" or some other effect. Additionally, the Presenter can convey the semantics of the query by reading the SQML to get hints on the categories that the query represents and the information type or context template. The Presenter can then use these hints to display semantic images and text consistent with the query, even though it has not received the results. The more hints the query has, the smarter the hourglass can get. The "Loading" page can then convey the atmosphere of "what is to come" - even before the actual results arrive from the Web service and are merged (if necessary) by the Presenter to yield the final results.
This "smart hourglass" can be displayed not just on the main results pane, but perhaps also on smart lens balloon popup windows and inline preview windows (essentially at every call site to the Web service and where there is "focus"). The Presenter can do the smart thing by timing out on the query (perhaps after several hundred milliseconds - the implementation should use usability tests to arrive at a figure for this) before displaying the "hourglass."

3. Visualizations -- Context Templates INTRODUCTION
Context templates are scenario driven information query templates that map to specific semantic models for information access and retrieval. Essentially, context templates can be thought of as personal, digital semantic information retrieval "channels" that deliver information to a user by employing a predefined semantic template. Context templates preferably aggregate information across one or more Agencies.
The context templates described below have been defined. Additional context templates, directed towards the integration and dissemination of varied types of semantic information, are contemplated (examples include context templates related to emotion, e.g., "Angry," "Sad," etc.;
context templates for location, mobility, ambient conditions, users tasks, etc.).
BREAKING NEWS
The Breaking News context template can be analogized to a personal, digital version of CNN's "Breaking News" program insert in how it conveys semantic information.
The context template allows a user to access information that is extremely time-critical from one or more Agencies, sorted according to the information creation or publishing time and a configurable amount of time that defines inf~rrnation criticality.
Figure 1Q2 is an illustration of a semantically appropriate image visualization for the Breaking News context template.
BREAKING NEWS - SAMPLE OBJECT AND CONTEXT BAR VISUALIZATIONS
Below is a list of sample or representative elements of visualizations appropriate to the Breaking News context. As with all Visualizations (or components thereof) in the preferred embodiment, the "mood" or semantic feeling or connotation will be appropriate to the specific context. By way of very rough analogy, the Visualization will be appropriate to the context within the application in the same way that a "set" must be appropriate to the particular scene in a screenplay for a movie. This will be true not only for this particular Object and Context Bar Visualization, but for all Visualizations in the preferred embodiment.

1. Ticking clock showing publication or scheduled time of most recent or pending breaking news item over a background of the total number of upcoming breaking news items 2. Ticking clock showing publication or scheduled time of most recent or pending breaking news item over semantic images) 3. Ticking clock showing publication or scheduled time of most recent or pending breaking news item over semantic images) and the total number of breaking news items 4. Ticking clock showing publication or scheduled time of most recent or pending breaking news item over a plain background 5. Non-ticking clocks showing publication or scheduled time of all breaking news items (sequentially) over various backgrounds 6. Calendar view showing publication or scheduled time of most recent or pending breaking news item over various backgrounds 7. Calendar view showing publication or scheduled time of all breaking news items (sequentially) over various backgrounds 8. Scaled font size - depending on the publication or scheduled time of the most recent or pending breaking news item 9. Scaled font size - depending on the number of breaking news items 10. Animated font (e.g., flashing text, rotating text, text on motion path, etc.) with animation rate depending on the publication or scheduled time of the most recent or pending breaking news item 11. Animated font (e.g., flashing text, rotating text, text on motion path, etc.) with animation rate depending on the number of breaking news items 12. Varying font color - depending on the publication or scheduled time of the most recent or pending breaking news item 13. Varying font color - depending on the number of breaking news items 14. Animated graphic of breaking news semantic images) or an equivalent 15. Number of breaking news items 16. Titles of breaking news items animated in a sequence (list view) 17. Titles and details of breaking news items animated in a sequence (tiled ~iev~) 18. Semantic image/motion moving on an orbital motion path around the object 19. Balloon popup showing number of items on semantic image/motion background 20. Balloon popup showing number of items with plain background but anmated with semantic image/motion HEADLINES
The Headlines context template can be analogized to a personal, digital version of CNN's "Headline News" program in how it conveys semantic information. The context template allows a user to access information headlines from one or more Agencies, sorted according to the information creation or publishing time and a configurable amount of time or number of items that defines information "freshness." For example, CNN's "Headline News"
displays headlines every 30 minutes (around the clock). In a preferred embodiment, the Headlines context template will be implemented as a SQL query on the server with the following sub queries chained in sequence: Recommendations Published Today, Favorites Published Today, Best Bets Published Today, Upcoming Events Occurring Today and Tomorrow, Annotated Items Published Today.
Preferably, all sub queries will be sorted by the publishing date/time and then be chained together. Additional filters will be applied to the query based on the predicate list in the SQML.
The foregoing principles are illustrated in Figure 103, which is a Headlines Visualization -Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
CONVERSATIONS CONTEXT TEMPLATE ' The Conversations context template can be analogized to a personal, digital version of CNN's "Crossfire" program in how it conveys semantic information. Like "Crossfire," which uses Conversations and debates as the context for information dissemination, in the preferred embodiment, the Conversations context template tracks email postings, annotations, and threads for relevant information.
The Conversations context template comprises the following information object types:
1. Email of a thread depth of at least one (An email reply to an email message) 2. Annotations of a thread depth of at least one (The annotation of an annotation of an object) 3. Internet liters Postings CA news posting reply to a news posting) The query will be sorted by thread depth. Additional filters will be applied to the query based on the predicate list in the SQML. In addition, the context skin should display the information items by thread.
Figure 104 is a Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Two People working at a desk) CONVERSATIONS CONTEXT - SAMPLE OBJECT AND CONTEXT BAR
VISUALIZATIONS
Below is a list of considerations for, or characteristics of visualization elements semantically appropriate to the corresponding indicated context (in parentheses).
1. Animated graphic of semantic image/motion(s) (icon and context guide view) view) 2. Maximum thread depth over plain background (icon and context guide view) 3. Maximum thread depth over semantic image/motion (icon and context guide 4. Titles of conversations animated in a sequence (list view) 5. Titles and details of conversations animated in a sequence (tiled view) 6. The number of conversations over a plain background (icon and context guide view) 7. The number of conversations over semantic image/motion(s) (icon and context guide view) Newsfnakers Context Ten2plate The Newsmakers context template can be analogized to a personal, digital version of NBC's "Meet the Press" program in how it conveys semantic information. In this case, the emphasis is on "people in the news," as opposed to the news itself or Conversations. Users navigate the network using the returned people as Information Object Pivots.
The Newsmakers context template can be thought of as the Headlines context template, preferably with the "People" or "Users" object type filters, and the "authored by," "possibly authored by," "hosted by," "annotated by," "expert on," etc. predicates (predicates that relate people to information).
The "relevant to" default predicate preferably is used to cover all the germane specific predicates. The sort order of the relevant information, e.g., the newsmakers, is sorted based on the order of the "news they make," e.g., headlines.
The query will be sorted by number of headlines. Additional filters will be applied to the query based on the predicate list in the SQML.
Figure 105 illustrates a semantic "Newsmaker" Visualization or Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
(Football Championship) NEWSMAKERS - SAMPLE OBJECT AND CONTEXT BAR VISUALIZATIONS
1. Animated graphic of 2 talking heads in conversation (icon and context guide view) view) 2. A~iimated graphic of semantic image/motion(s) (icon and context guide view) 3. Total number of newsmakers (icon and context guide view) 4. Total number of newsmakers over semantic image/motion (icon and context guide 5. Names of newsmakers animated in a sequence (list view) 6. Names and details of newsmakers animated in a sequence (tiled view) is7 Upcoming Events Context Template The Upcoming Events context template (and its resulting Special Agent) can be analogized to a personal digital version of special programs that convey information about upcoming events. Examples include specials for events such as "The World Series," "The NBA
Finals," "The Soccer World Cup Finals," etc. The equivalent in a knowledge-worker scenario is a user that wants to monitor all upcoming industry events that relate to one or more categories, documents or other Information Object Pivots. The Upcoming Events context template is preferably identical to the Headlines context template except that only upcoming events are filtered and displayed (preferably using a semantically appropriate "context Skin" that connotes events and time criticality). Returned objects are preferably sorted based on time criticality with the most impending events listed first.
Figure 106 illustrates a semantic "Upcoming Events" Visualization - Sample rmage for smas-t hourglass, interstitial page, transition effects, background chrome, etc. (Appointment Binder).
UPCOMING EVENTS - SAMPLE OBJECT AND CONTEXT BAR VISUALIZATIONS
1. Ticking clock showing time till next event over a background of the total nmnber of upcoming events (icon and context guide view) 2. 'Ticking clock showing time till next event over semantic image/motion(s) (icon and context guide view) 3. Ticking clock showing time till next event over semantic image/motion(s) and the total number of upcoming events (icon and context guide view) 4. Ticking clock showing time till next event over a plain background (icon and context guide view) 5. Non-ticking clocks showing time till all upcoming events (sequentially) over various backgrounds (icon and context guide view) 6. Calendar view showing scheduled time of next upcoming event over various backgrounds (icon and context guide view) 7. Calendar view showing scheduled time of all upcoming events (sequentially) over various backgrounds (icon and context guide view) Animated graphic showing calendar motion (icon and context guide view) 9. Animated graphic of semantic image/motion(s) (e.g., schedule book) (icon and context guide view) 10. The total number of upcoming events over semantic image/motion(s) (icon and context guide view) 15s 11. The total number of upcoming events over a plain background (icon and context guide view) 12. Titles of upcoming events animated in a sequence (list view) 13. Titles and details of upcoming events animated in a sequence (tiled view) Discovery The Discovery context template can be analogized to a personal, digital version of the "Discovery Channel." In this case, the emphasis is on "documentaries" about particular topics.
The Discovery context template simulates intelligent aggregation of information by randomly selecting information objects that relate to a given set of categories and which are posted within an optionally predetermined, configurable time period. The semantic weight as opposed to the time is the preferred consideration for determining how the information is to be ordered or presented. The context template can be implemented by filtering all information types by the semantic link strength for the categorization predicate. In this case, the filter should be less selective than the 'Best Bets' filter - the context template lies somewhere between 'Best Bets' and 'All Items' in terms of filtering.
Figure 107 is a "Discovery" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Petri Dish).
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VII~~JA.I~LI~.~TT~~I'T~
1. l~nimated graphic of semantic image/motion(s) (e.g., a telescope, a voyager spacecraft, an old ship at sea) (icon and context guide view) 2. Titles of the first N information items in a sequential animation (list view) 3. Titles and details of the first N information items in a sequential animation (tiled view) 4. The total number of items over semantic image/motion(s) (icon and context guide view) 5. The total number of items (icon and context guide view) History The History context template can be analogized to a personal, digital version of the "History Channel." In this case, the emphasis is on disseminating information not just about particular topics, but also with a historical context. For this template, the preferred axes are category and time. The History context template is similar to the Discovery context template, further in concert with "a minimum age limit." The parameters are preferably the same as that of the Discovery context template, except that the "maximum age limit" parameter is replaced with a "minimum age limit" parameter (or an optional "history time span"
parameter). In addition, returned objects are preferably sorted in reverse or random order based on their age in the system or their age since creation.
Figure 108 illustrates a semantic "History" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (War Memorial).
HISTORY - SAMPLE OBJECT AND CONTEXT BAR ANIMATIONS
VISUALIZATIONS
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Titles of the oldest (or random) N information items in a sequential animation (list view) 3. Titles and details of the oldest (or random) N information items in a sequential animation (tiled view) 4. Total number of items over semantic image/motion(s) (icon and contea~t guide view) 5. Total number of items over plain baclcground (icon and context guide view) All IteYns The All Items context template represents context that returns any information that i~
relevant based on either semantics or based on a keyword or text based search.
In this case, the emphasis is on disseminating information that may be even remotely relevant to the context. The primary axis for the All Items context template is preferably the mere possibility of relevance. In the preferred embodiment, the All Items context template employs both a semantic and text-based query in order to return the broadest possible set or universe of results that may be relevant.
Figure 109 illustrates a semantic Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (~uter Space).
ALL ITEMS - VISUALIZATION & SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Animated graphic of semantic imagelmotion(s) or an equivalent 2. 'Titles of the most recent 1V mtormarion items in a sequential animation (list view) 3. Titles and details of the most recent N information items in a sequential animation {tiled view) 4. Total number of items over semantic image/motion(s) (icon and context guide view) 5. Total number of items over plain background (icon and context guide view) Best Bets The Best Bets context template (and its resulting Special Agent) represents context that returns only highly relevant information. In a preferred embodiment, the emphasis is on disseminating information that is deemed to be highly relevant and semantically significant. For this context template, the primary axis is relevance. In essence, the Best Bets context template employs a semantic query and will not use text based queries since it cannot guarantee the relevance of text-based query results. The Best Bets context template is preferably initialized with a category filter or keywords. If keywords are specified, the server performs categorization dynamically. Results are preferably sorted based on the relevance score, or the strength of the "belongs to category" semantic link from the object to the category filter.
Figure 110 illustrates a "Best Bets" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. {Microscope).
BEST BET VISUAI,I~A'fIO~T - SAl~hI~E Ol~~~'I'" A~IsTI~ ~~~1'~T~I"E~'1C" ~~.,f~
~I'JIII~~°~Itt~?l~~
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Titles of the most recent N information items in a sequential animation (list view) 3. Titles and details of the most recent N information items in a sequential animation (tiled view) 4. Total number of items over semantic image/motion(s) {icon and context guide view) 5. Total number of items over plain background {icon and context guide view) FAVORITES
The Favorites context template (and its resulting Special Agent) represents context that returns "favorite" or "popular" information. In this case, the emphasis is on disseminating information that has been endorsed by others and has been favorably accepted.
In the preferred embodiment, the axes for the Favorites context template include the level of readership interest, the "reviews" the object received, and the depth of the annotation thread on the object. In one embodiment, the Favorites context template returns only information that has the "favorites"
semantic link, and is sorted by counting the number of "votes" for the object (based on this semantic link).
Figure 111 illustrates a semantic Visualization- Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (coffee and pastry).
FAVORITES VISUALIZATION - SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Titles of the most recent N information items in a sequential animation (list view) 3. Titles and details of the most recent N information items in a sequential animation (tiled view) 4. Total number of items over semantic image/motion(s) (icon and context guide view) 5. Total number of items over plain background (icon and context guide view) C1LA~~Ir'L~
The Classics context template (and its resulting Special Agent) represents context that returns "classical" information, or information that is of recognized value.
Like the Favorites context template, the emphasis is on disseminating information that has been endorsed by others and has been favorably accepted. For this context template, the preferred axes include a historical context, the level of readership interest, the "reviews" the object received and the depth of the aimotation thread on the obj ect. The Classics context template is preferably implemented based on the Favorites context template but with an additional minimum age limit filter and voting score, essentially functioning as an "Old Favorites" context template.
Figure 112 illustrates a semantically appropriate Sample Image for "Classics"
for smart hourglass, interstitial page, transition effects, background chrome, etc.
(Car) CLASSICS VISUALIZATIONS - SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Titles of the most recent N information items in a sequential animation (list view) 3. Titles and details of the most recent N information items in a sequential animation (tiled view) 4. Total number of items over semantic image/motion(s) (icon and context guide view) 5. Total number of items over plain background (icon and context guide view) ' RECOMMENDATIONS
The Recommendations context template represents context that returns "recommended"
information, or information that the Agencies have inferred would be of interest to a user.
Recommendations will be inserted by adding "recommendation" semantic links to the "SemanticLinks" table and by mining the favorite semantic links that users indicate.
Recommendations are preferably made using techniques such as machine learning and collaborative filtering. The emphasis of this context template is on disseminating information that would likely be of interest to the user but which the user might not have already seen. For this context template, the primary axes preferably include the likelihood of interest and freshness.
Figure 113 illustrates a semantically appropriate "Recommendation"
Visualization -Sample Image for the contextual/application elements of smart hourglass, interstitial page, transition effects, background chrome, etc. (Thumbs up).
~~I~I~E1~~A'~CI~~~T ~I~IlALII~ATI~I~T - ~A1~~~E ~E~1ECT AI'~T~ ~~I'~T'~IE~~T' EA 11~
AI\TI1~ATI~I'J~
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Titles of the most recent N information items in a sequential animation (list view) 3. Titles and details of the most recent N information items in a sequential animation (tiled view) 4. Total number of items over semantic image/motion(s) (icon and context guide view) 5. Total number of items over plain background (icon and context guide view) TODAY
The Today context template represents context that returns information posted or holding (in the case of events) "today." The emphasis with this context template is preferably on disseminating information that is deemed to be current based on "today" being the filter to determine freshness.
Figure 114 illustrates a semantic "Today" Visualization - Sample Image for the elements smart hourglass, interstitial page, transition effects, background chrome, etc.
"TODAY VISUALIZATION" - SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Titles of the most recent N information items in a sequential animation (list view) 3. Titles aald details of the most recent N information items in a sequential animation (tiled view) 4. Total number of items over semantic imagelmotion(s) (icon and context guide view) 5. Total number of items over plain background (icon and context guide view) ANNOTATED ITEMS
The Annotated Items context template represents context that returns annotated information. The emphasis with this context template is on disseminating information that is likely to be important based on the fact that one or moxe users have annotated the items.
Figure 115 illustrates a semantic "Annotated Items" Visualization -- Sample Image for smart hourglass, interstitial page, transition effects, backgr~und chrome, etc..
"~I'JI'~1~Th'~°EID IITEP~~" ~I~U~I~A'1CI~I'~T - ~~I~1IPILE ~BJ1EC'1C
~1'~T1D ~L~1'~T'If°E~'Ir EAR
AI'~TII~Ift~Ty01~~15 1. A~Zimated graphic of semantic imagelmotion(s) or an equivalent 2. Titles of the most recent N information items in a sequential animation (list view) 3. Titles and details of the most recent N information items in a sequential animation (tiled view) 4. Total number of items over semantic image/motion(s) (icon and context guide view) 5. Total number of items over plain background (icon and context guide view) ANNOTATIONS
The Annotations context template represents context that returns annotated information.
The emphasis with this context template is on disseminating information that are annotations.
Figure 116 illustrates a semantic Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc. (Note pinned to Bulletin Board) "ANNOTATIONS" VISUALIZATION - SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Titles of the most recent N information items in a sequential animation (list view) 3. Titles and details of the most recent N information items in a sequential animation (tiled view) 4. Total number of items over semantic image/motion(s) (icon and context guide view) 5. Total number of items over plain background (icon and context guide view) EXPERTS
Figure 117 illustrates a semantic "Experts" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
(Professor) "EXPERTS" VISUALIZATION - SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Animated graphic of semantic im~ge/n~otion(s) or an equi~ralent 2. Names of the most recent 1~ experts in a sequential animation (list view) 3. Names and details of the most recent N experts in a sequential animation (tiled view) 4. Total number of experts over semantic image/motion(s) (icon and context guide view) 5. Total number of experts over plain background (icon and context guide view) PLACES
Figure 118 illustrates a semantic "Places" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
(Paris) "PLACES" VISUALIZATION - SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Names of the most recent N places in a sequential animation (list view) 3. Names and details of the most recent N places in a sequential animation (tiled view) 4. Total number of places over semantic image/motion(s) (icon and context guide view) 6. Total number of places over plain background (icon and context guide view) BLENDERS
Figure 119 illustrates a semantic "Blenders" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc.
(Blenders) "BLENDERS" VISUALIZATION - SAMPLE ICONIC ANIMATIONS
1. Animated graphic of semantic image/motion(s) or an equivalent 2. Animated graphic of blender or mixer in action 3. Titles of the blender items in a sequential animation (list view) 4. Titles and details of the blender items in a sequential animation (tiled view) 5. Total number of items over semantic image/motion(s) (icon and context guide view) 6. Total number of items over plain background (icon and context guide view) II'~Tl~~~I~'11I~I'~T ~l~JlECT T~PlE~
Figures 120 through 138 illustrate semantic Visualizations for the following Information Object Types, respectively: Documents, Books, Magazines, Presentations, Resumes, Spreadsheets, Text, Web pages, White Papers, Email, Email Annotations, Email Distribution Lists, E~rents, Meetings, Multimedia, Onlina Courses, People, Customers, and Users.
PRESENTATION SI~.iN T'A'PES
TIMELINE
Figure 139 illustrates a semantic "Timeline" Visualization - Sample Image for smart hourglass, interstitial page, transition effects, background chrome, etc..
"TIMELINE" VISUALIZATION - SAMPLE OBJECT AND CONTEXT BAR
ANIMATIONS
1. Calendar view showing effective time (publication time, scheduled time, etc.) of information item over various backgrounds (icon and context guide view) 2. Calendar view showing effective time of all information items (sequentially) over various backgrounds (icon and context guide view) 3. Animated graphic showing calendar motion (icon and context guide view) 4. Animated graphic of semantic image/motion(s) (e.g., time warp imagelmotion) (icon and context guide view) 5. The total number of information items over semantic imagelmotion(s) (icon and context guide view) 6. The total number of information items over a plain background (icon and context guide view) 7. Titles of information items animated in a sequence (list view) 8. Titles and details of information items animated in a sequence (tiled view) 9. Scrolling, linear timeline control with items populated based on effective date/time 10. Animated timeline ticker control sorted by effective date/time The Power of Semantic Visualizations.
One final note concerning Visualizations. The preferred embodiment not only searches for information semantically, and not only organizes and stores it semantically, it also presents it semantically. And, the presentation is not semantic only in the sequence, organization and relationships of the information, but also visually, as the foregoing Visualizations are, in part, intended to convey. As a result, the user is aided in understanding the information being presented by the system in roughly in the same way that a viewer of a movie is aided in understanding the meaning of dialogue by the surrounding context of the lighting, costume, music and entire set or scene. Put differently, the Visualizations, as with everything else presented or managed by, or located with, the preferred embodiment system, serape the purpose of conveying meaningful information; or, just as aptly, to convey information meaningfully.
Ilileaning is a unifying theme of the preferred embodiment; it permeates the design and operation of the system, and each constituent component part of which the system is comprised.
While the preferred and some alternate embodiments of the invention have been illustrated and described, as noted above, many changes can be made without departing from the spirit and scope of the invention. Accordingly, the scope of the invention is not limited by the disclosure of the preferred embodiment. Instead, the invention should be determined entirely by reference to the claims that follow.

Appendix A
SYSTEM AND METHOD FOR KNOWLEDGE RETRIEVAL, MANAGEMENT, DELIVERY AND PRESENTATION
1 O IN VENT~R
Nosa 0moigui P°RIO~,ITY CLAInI
This application claims priority from earlier filed U.S. Provisional Patent Application Serial No. 60/300,385 filed June 22, 2001 and U.S. Provisional Patent Application Serial No. d0/3~O,C10 filed February 28, 2002.
COPYRIGHT NOTICE
This disclosure is protected under tlnited States and International Copyright Laws.
~ 2002 Nosa 0moigui. All Rights Reserved. A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

This invention relates generally to information management systems and, more specifically, to an integrated and seamless implementation framework and resulting medium for knowledge retrieval, management, delivexy and presentation.

BACKGROUND OF THE INVENTION
Knowledge is now widely recognized as a core asset for organizations around the world, and as a tool for competitive advantage. In today's comlected, information-based world, knowledge-workers must have access to the knowledge and the tools they need to make better, faster, and more-informed decisions to improve their productivity, enhance customer relationships, and to make their businesses more competitive. In addition, industry observers have touted "agility" and the "real-time enterprise" as important business goals to have in the information economy.
Many organizations have begun to realize the value of disseminating knowledge within their organizations in order to improve products and customer service, and the value of having a well-trained workforce. The investments businesses are making in e-Learning and corporate training provides some evidence of this. Companies have also invested in tools for content management, search, collaboration, and business intelligence.
Companies are also spending significant resources on digitizing their business processes, particularly with respect to acquiring and retaining customers.
However, many knowledge/learning and customer-relationship assets are still stored in a diverse set of repositories that do not understand each other's language, and as a result are managed and interacted with as independent islands of information. As such, what many organizations call "knowledge" is merely data and information. The inforn~ation economy in large part is a struggle to find a way to provide context, meaning and efficient access to this ever increasing body of data and information. Or, stated differently, to turn the mass of available data and information into usable knowledge.
Information has been long accessible in a variety of forms, such as in newspapers, books, radio and television media, and in electronic form, with varying degrees of proliferation. Information management and access changed dramatically with the use of computers and computer networks. Networked computer systems provide access throughout the system to infornlation maintained at any point along the system. Users need only establish the requisite connection to the network, provide proper authorization and identify the desired information to obtain access.
Information access further improveoi with the advent of the Internet, which connects a large number of computers across diverse geography to provide access to a vast body of information. The most wide spread method of providing information over the Internet is via the World Wide Web. The Web consists of a subset of the computers or Web servers connected to the Internet that typically 'run Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), GOPHER or other servers. Web servers host Web pages at Web sites. Web pages are encoded using one or more languages, such as the original Hypertext Markup Language (HTML) or the more current eXtensible Markup Language (?AML) or the Standard Generic Markup Language (SGML). The published specifications for these languages are incorporated by reference herein. Web pages in these formatting languages may be accessed by Internet users via web browsing software such as Microsoft's Internet Expl~rer or 1'letscape's hTavigator.
The Web has largely been organized based on syntax and structure, rather than context and semantics. As a result, information is typically accessed via search engines and Web directories. Current search engines use keyword and corresponding search techniques that rely on textual or basic subject matter inforniation and indices without associated context and semantic information. Unfortunately, such searching methods produce thousands of largely unresponsive results; documents a:; opposed to actionable knowledge.
Advanced searching techniques have been developed to focus queries and improve the relevance of search results. Many such techniques rely on historical user search trends to make basic assumptions as to desired information. Alternatively, other search techniques rely on categorization of Web sites to further focus i:he search results to areas anticipated to be most relevant. Regardless of the search technique, the underlying organization of searchable i7o information is index-driven rather than context-driven. The frequency or type of textual information associated the document detencnines the search results, as opposed to the attributes of the subject matter of the document and how those attributes relate to the user's context. The result is continued ambiguity and inefficiency surrounding the use of the Web as a tool for acquiring actionable knowledge.
In enterprises around the world today, the Web is the information platform for knowledge-workers. And there lies the problem. The Web asvve know it is a platform for data and information while its users operatE; at the level of "knowledge."
This disconnect is a very fundamental one and carrot be understated. The Web, in large measure, has fulfilled the dream of "information at your fingertips." However, knowledge-workers demand "knowledge at your fingertips" as opposed to mere "Illf~rmatlon at your fingertips."
Unfortunately, today's knowledge-workers use the Web to browse and search for documents-compilations of data and information-rather than actual knowledge relevant to their inquiry. To achieve improved knowledge requires providing proper context, meaning and efficient access to data and information, all of which are missing with the traditional Web.
Efforts have been made to achieve the goal of "knowledge at your fingertips."
One example is a new concept for information organization and distribution referred to as the Semantic Web. The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.
While conceptually a significant step forward in supporting improved context, meaning and access of information on the Internet, the Semantic Web has yet to find successful implementation that lives up to its stated potential.
Both the current Web and the Semantic Web fail to provide proper context, meaning and efficient access to data and information to allow users to acquire actionable knowledge.
This is partially a problem related to the ways in which Today's Web and the contemplated Semantic Web are structured or, in other words, related to their teclmology layers. As shown in FIGURE 1, Today's Web, for example, which is a hypertext medium, provides the three technology layexs, which include "dumb" links, or links having no context-sensitivity, tlIlle-SeIISItIVIty, etc. Present conceptualizations of the Semantic Web, also referred to as a "semantic hypermedia," provide for five 'technology layers, as shown in FIGURE
2. As explained in greater detail below, there area serious limitations associated with each of the technology layer structures.
In addition, various properties must be. present in a comprehensive infornlation management system to provide an integrated and seamless implementation framework and resulting medium for knowledge retrieval, management and delivery. A non-exhaustive list of these properties include: Semantics/h~eaning; Context-Sensltmty; Time-Sensitivity;
Automatic and intelligent Discoverability; Dynamic Linking; User-Controlled Navigation and Browsing; Non-HTML and Local Document Participation in the Network;
Flexible Presentation that Smartly Conveys the Semantics of the Information being Displayed; Logic, Inference, and Reasoning; Flexible User-Driven Information Analysis; Flexible Semantic Queries; Read/Write Support; Annotations; "Web of Trust"; Information Packages ("Blenders"); Context Templates, and User-CJriented Information Aggregation.
Each of these properties will be discussed below in the context of their application to both Today's Web and the Semantic Web.

Today's Web lacks semantics as an intrinsic part of the platfornl and user experience.
Web pages convey only textual and graphical data rather than the semantics of the data they contain. As a result, users cannot issue semantic queries such as those that one might expect with natural language-for example, "find nue all books less than hundred pages long, about Latin Jazz, and published in the last five years." To be able to process such a query, a Web site or search engine must "know" it contains. books and must be able to intelligently filter its contents based on the semantics of the query request. Such a query is not possible on the Web today. Instead, users are forced to rely on text-based searches. These searches usually result in information overload or information loss because the user is forced to pick search terms that 1171ght not match the text in the information base. In the afOreI11e11t10ned example, a user might pick the search term "Books Latin Jazz" and hope that the search engine can make the connection. The user is usually then left to independently filter the search results. This sort of text=based search also implies that teens that might convey the same meaning. In the above example, results from search teens such as "Books on .South or Central American Jazz" or "Publications on Jazz from Latino Lands" might be ignored during the processing of the search query.
The lack of semantics also implies that Today's Web does not allow users to navigate based on they way humans think. For example, one might want to navigate a corporate intranet using the organizational structure. For example, from people to the documents they create to the experts on that documents to the direct reports of those experts to the distribution lists the direct reports are members of to the members of the distribution lists to the documents those members created, etc. This "web" is semantic and is based on actual information classification ("things") and not: just "pages" as Today's Web is.
The lack of semantics also has other implications. First, it means that the Web is not programmable. With semantics, the Web can be consumed by Smart Agents that can make sense of the pages and the links and then make inferences, recommendations, etc. With Today's Web, the only "Agent" that can make inferences is the human brain. As such, the Web does not employ the enormous processing power that computers are capable of-because it is not represented in a way that computers can understand.
The lack of semantics also implies that information is not actionable. A
search engine does not "understand" the results it spits out. As such, once a user receives search results, he or she is "on his or her own." Also, a web browser does not "understand" the information it is displaying and as such cannot d0 Slllart th117gS Wlth the information. With semantics in place, a smart display, for example, will "know" that an event is an event and might do interesting things like check if the event is already in the user's calendar, display free/busy information, or allow the user to automatically insert tlae event into his/her calendar thereby making the information actionable. IIlfOn11at10t1 presented without SelllantlCS 15 nOt aCtlOnable Or Illlght require that the semantics be inferred, which might result in an unpleasant user experience.
The Semantic Web seeks to address semantics/meaning limitations with Today's Web by encoding information with well-defined semantics. Web pages on the Semantic Web include metadata and semantic links to otloer metadata, thereby allowing search engines to perform more intelligent and accurate searches. In addition, the Semantic Web includes ontologies that will be employed for knowledge representation, thereby allowing a semantic search engine to interpret terms based on n-leaning and not merely on text.
For example, in the previous example, Latin Jazz ontology might be employed on a Semantic Web site and would allow a search engine on the site to ":know" that the terms "Books on South or Central l~merican Jazz" or "Publications on Jazz from Latino Lands" have the same meaning as the term "Books on Latin Jazz." While concepi:ually overcoming many of the deficiencies with Today's Web, there has not to date been a successful implementation of a well-defined data model providing context and meaning, including in particular the necessary semantic links, ontologies, etc. to provide for additional characteristics such as context-sensitivity and time-sensitivity.
CONTEAT-SENSITIVITY
Today's Web lacks context-sensitivity. The implication of a lack of context is that Today's Web is not personal. For example, documents in accessible storage are independently static and therefore stupid. Irlfornzation relevant to the subject matter of the document has already been published, is being newly published, or will soon be published.
Because the document in storage is static, however, there is no way to dynamically associate its subject matter with this relevant information in real-tinge. Stated differently, users have no way to dynamically connect their private context with external information in real-time.
Information sources (such as the document) that form context sit in their own islands, totally isolated from other relevant information sources. This results in information and productivity losses.
The primary reason for this is that Today's Web is a presentation-oriented medium designed to present views of information to a dumb client (e.~., remote computer). The client has virtually no role to play in the user experience, aside from merely displaying what the server tells it to display. Even in cases where there is client-side code (like Java applets and ActiveX controls), the controls usually do one specific thing and do not have coordinated action with the remote server such that coda on the client is being orchestrated with code on the server.
From a productivity standpoint, the implication of this is that knowledge-workers and inforniation consumers are totally at the mercy of information authorse 'Today, knowledge.-workers have portals that are maintained and updated to provide custom views of corporate information, external data, etc. However, this is still very limiting because knowledge-workers are completely helpless if nothing dynamically and intelligently connects relevant information in the context of their task with information that users have access to.
If a knowledge-worker does not see a link to a relevant piece of information on his of her portal, of if a friend or colleague does not email him or her the link, the information gets dropped; infomlation does not connect with or adapt to the user context or the context in which it is displayed. Likewise, it is not enough to just notify a user that new data for an entire portal is available and shove it down to their local hard drive. It lacks a customizable presentation with context sensitive alert notif cations.
' 'vThe Semantic Web suffers from the same limitations as Today's Web when it comes to context-.sensitivity. On the Semantic Wets, users are likewise at the mercy of information i7s authors. The Semantic Web itself will be authored, but the authoring will include semantics. ' As a result, users are still largely on their own to locate and evaluate the relevance of available information. The Semantic .Web, as a standalone entity, will not be able to make these dynamic connections with other information sources.
S TIME-SENSITIVITY
Today's Web lacks time-sensitivity. The Web platform (e.g., browser) is a dumb piece of software that merely presents information, without any regard to the time-sensitivity of the information. The user is left to infer time sensitivity or do without it. This results in a huge loss in productivity because tlae Web ;platform cannot make time-sensitive connections in real-time. While some Web sites focus on presenting time-sensitive information, for example, by indexing information past a predetermined date, the Web browser itself has no notion of time-sensitivity. Instead, it is left to individual Web sites to include time-sensitivity in the information they display in their own island. In other words, there is no axis of time on a Web link.
1 S Tlm ~er~antgc °~~eb9 like Today's Web, also does not address time-sensitivity.
A Semantic Web can have semantic links that do not internalize time. This is largely because the Semantic Web implicitly has no notion of software Web services that address context and time-sensitivity.

?0 Today's 'Web lacks automatic anti intelligent discoverability of newly created information. There is currently no way to know what Web sites started anew today or yesterday. Unless the user is notified or the user serendipitously discovers a new site when he or she does a search, he or she might not have any clue as to whether there are any new Web sites or pages. The same problem exists in. enterprises. On Intranets, knowledge-workers 25 have no way of knowing when new Web sites come up unless informed via some external means. The Web platform itself has no notion of announcements or discovery. In addition, there is no context-sensitive discovery to determine new sites or pages within the context of the user's task or current information space.
The Semantic Web, like Today's Web, does not address the lack of automatic discoverability. Semantic, Wel? sites suffer from the, same prOblelll-uSerS
either Wlll have to find out about the existence of new information sources from external sources or'~tlrough personal discovery when they perform a search.
DYNAI'11C LINKING
Today's Web employs a pure network or graph "data structure" for its information model. Each Web page represents a node in the network and each page can contain links to other nodes in the network. Each link is manually authored into each page.
This has several problems. First, it means that the network needs to be maintained for it to have continuous value. If Web pages are not updated or if Web page or site authors do not have the discipline to add links to their pages based on relevance, the network loses value.
Today's Web is essentially prone to having dead links, old links, etc. Another problem with a pure network or graph information model is that the information consumer is at the mercy of-rather than in control of-the presentation of the Web paf;e or site. In other words, if a Web page or site does not contain any links, the user has no recourse to find relevant information. Search engines are of little help because they merely return pages or nodes into the network. The network itself does not have any independent or dynamic linking ability. Thus, a search engine can easily return links to Web pages that themselves have no links or dead, stale or irrelevant links. Once users obtain search results, they are on their own and are completely at the mercy of whether the author of the returned pages inserted relevant, time-sensitive links into the page.
The Semantic Web suffers from the same' problem as Today's Web because the Semantic Web is merely Today's Web plus semantics. Even though users will be able to navigate the 'network semantically (which they cannot currently do with the Web), they will still be at the mercy of how the information has been authored. In other words, the Semantic Web is also dependent on the discipline of the authors and hence suffers from the same aforementioned problems of Today's Web. If the Semantic Web includes pages with ontologies and metadata, but those pages are not well maintained or do not include lines to other relevant sources, the user will still be unable to obtain current links and other information. The Semantic Web, as currently contemplated, will not be a smart, dynamic, self authoring, self healing network.
USER-CONTROLLED NAVIGATION AND BROWSING
With Today's Web, the user ha s no control over the navigation and browsing experience, but rather is completely at the mercy of a Web page and how it is authored with links (if any). As shown with reference tea prior art FICiUI~E 3, Today's Web consists of "dumb links," or statically authored generic links that are wholly dependent on continuous maintenance to be navigable.
The Semantic Web suffers from a similar problem as Today's Web in that there is no user-controlled browsing. Instead, as shown with reference to prior art FICaLJRE 4~, the Semantic Web consists of "dumb links," further including....semantic information and metadata. However, the Semantic Web links remain equally dependent on continuous maintenance to be navigable.
NON-HTML AND LOCAL DOCUnIENT PARTICIPATION IN THE NETWORK
Another problem with Today's Web is the requirement that only documents that are authored as HTML can participate in the Web, in addition to the fact that those documents have to contain links. The implication is that other information objects like non-HTML
documents (e.g., PDF, Microsoft Word, PowerPoint, and Excel documents, ete.)-especially those on users" hard drives-are excluded from the benefits of linking to other objects in the network. This is very limiting, especially since there might be semantic relevance between information objects that are not HTML and vrhich do not contain links.
17s Furthermore, search engines do not return results for the entire universe of information since vast amount of content available on the web is inaccessible to standard web crawlers. This includes, for example, content stored in databases, unindexed file repositories, subscription sites, local machines and devices, proprietary file formats (such as Microsoft 5Office documents and email), and non-text multimedia files. These form a vast constellation of inaccessible matter on the Internet, referred to as "the invisible Intranet" inside corporations. Today's Web servers do not provide web crawler tools that address this problem.
The Semantic Web also suffers from this limitation. It does not address the millions of non-HTML documents that are already out there, especially those on users"
hard drives.
The implication is that docwnents that do not have RI)F metadata equivalents or proxies cannot be dynamically linked to the network:.

INFORMATION BEING DtSPLAYEH
T°oday'~ Web does not allow users to customise or "skin" a Web site or page. This is because Today's Web servers return information that is already formatted far presentation by the browser. The end user has no flexibility in choosing the best means of displaying the information-based on different criteria (e.g., the type of infornlation, the available amount of real estate, etc.) The Semantic Web does not address the issue of flexible presentation. While a semantic Web site conceptually employs I2DF and ontologies, it still sends HTML to the browser. Essentially, the Semantic Web does not provide for specific user empowennent for presentation. As such, a Semantic Web site, viewed by Today's Web platform, will still not empower the user with flexible presentation. Moreover, despite industry movement towards XML,., only a new platform can dictate that data will be separated from presentation and define guidelines for making the data programmable. Authors building content for the Semantic Web either return XML and avoid issues with presentation entirely, or focus their efforts on a single presentation style (vertical industry scenario) for rendering. Neither approach allows the Semantic Web to achieve an optimum degree of knowledge distribution.
LOGIC, TNFERENCE AND REASONING
Because Today's Web does not have any semantics, metadata, or knowledge representation, computers cannot process Web pages using logic and inference to infer new links, issue notifications, etc. Today's Web was designed and built for human consumption, not for computer consumption. As such, Today's Web cannot operate on the information fabric without resorting to brittle, unreliable techniques such as screen scraping to try to extract metadata and apply logic and inference.
While the Semantic Web conceptually uses metadata and meaning to provide Web pages and sites with encoded information that can be processed by computers, there is no current implementation that is able to successfully achieve this computer processing and which illustrates new or improved scenarios that benefit the information consumer or 1 S producer.
FLE7~IBLE USER-DRIVEN INi°OR111AT10N A1VALYSIS
Today's Web lacks user-driven information analysis. Today's Web does not allow users to display different "views" of the links, using different filters and conditions. For example, Web search engines do not allow users to test the results of searches under different scenarios. Users camiot view results using different pivots such as information type (e.g., documents, email, etc.), context (e.g., "Headlines," "Best Bets," etc.), category (e.g., "wireless," "technology," etC.) etC.
While providing a greater degree of flexible information analysis, the Semantic Web does not describe how the presentation layer can interact with the Web itself in an interactive fashion to provide flexible analysis.
1so FLExII3LE SEMANTIC QUERIES
Today's VVeb only allows text-based queries or queries that are tied to the schema of a particular Web site. These queries lack :flexibility. Today's Web does not allow a user to issue queries that approximate natural language or incorporate semantics and local context.
Foi: example, a query such as "Find me all email messages written by my boss or anyone in ,,;
research and which relate to this specification on my hard disk" is not possible with~Today's Web.
By employing metadata and ontolo dies, the conceptual Semantic Web allows a user to issue more flexible queries than Today's Web. For example, users will be able to issue a query such as "Find me all email messages written by my boss or anyone in research."
However, users will not be able to incorporate local context. In addition, the Semantic Web does not define an easy manner with which users will query the Web without using natural language. Natural language technology is an option but is far from being a reliable technology. As such, a query user interface that approximates natural language yet does not rely on natural language is requirede The Semal~tic Web does not address thiso IDEA~JWRITE SUPP~RT
Today's Web is a read-only Web. For example, if users encounter a dead link (e.g., via the "404" error), they caln~ot "fix" the link by pointing it to an updated target that Illlght be known to the user. This can be limiting, especially in cases where users might have important knowledge to be shared with others and where users might want to have input as to how the network should be represented and f;volve.
While the Semantic Web conceptually allows for read/write scenarios as provided by independent participating applications, there is no current implementation that provides this ability.
1s1 ANNOTATIONS
Today's Web has no implicit support for annotations. And while some specific Web sites support annotations, they do so in a~ very restricted and self contained way. Today's Web medium itself does not address annotations. In other words, it is not possible for users to annotate any link with their comments or additional information that they have access to.
This results in potential information loss.
While the Semantic Web conceptually allows for annotations to be built into the system subject to security constraints, there is no current implementation that provides this ability.
"WEe or. TRUST"
Today's Web lacks seamless integration of authentication, access control, and authorization into the Web, or what has been referred to as a "Web of Trust:"
With a Web of Trust, far example, users are able to make assertions, fix and update links to the Web and have access control restrictions built in for such operations. On Today's Web this lack of trust also means that ~jeb se.p-~rices remain independent islands that musfi implement a proprietary user subscription authorization, access control or payment system.
Grand schemes for~centralizing this information on 3rd party servers meet with consumer and vendor distrust because of privacy concerns. To gain access to rich coritent, asset users must log in individually and provide identity information at each site.
Zfl While the Semantic VVeb conceptually allows for a Web of Tnist, there is no current implementation that provides for this ability.
INFORIIIATION PACKAGES (BLENDERS) Neither Today's Web nor the Semantic Web allows users to deal with related semantic information as a whale unit by combining characteristics of potentially divergent semantic information to produce overlapping results (for example, like creating a custom, personal newspaper or TV channel).
1s2 r CONTEXT TEMPLATES
Neither Today's Web nor the Semantic Web allows users to independently create and map to specific and familiar semantic rnodels for information access and retrieval.
USER-ORIENTED INFORMATION AGGREGATION
Today's Web lacks support for user-oriented information aggregation. The user can only access one Web site or one search engine at a time, within the context ~of one browsing session. As such, even if there is context or time-sensitive infonnation on other information sources that relate to the information that the user is currently viewing, those sources cannot be presented in a holistic fashion in the current context of the user's task.
10. The Semantic Web also suffers from a lack of user-oriented infornlation aggregation.
The medium itself is an extension of Today's Web. As such, users will still access one site or one search engine at a time and will not be able to aggregate information across infornlation repositories in a context or time-sensitive manner.
Given the growing demand for "knowledge at your fingertips" as well as the deficiencies in Today~s Web and the conceptual Semantic ~eb9 many of which are noted above, there is a need for a new and comprehensive system and method of knowledge retrieval, management and delivery.
SU1~9MARY OF THE INVENTION
The present invention is directed in part to an integrated and seamless implementation framework and resulting medium for knowledge retrieval, management, delivery and presentation. The system includes a server comprised of several components that work together to provide context and time-sensitive semantic information retrieval services to clients~operating a presentation platform via a communication medium. The server includes a first server component that is responsible for adding and maintaining domain-specific semantic information ° or intelligence. The first server component preferably includes structure or methodology directed to providing the following: a Semantic Network, a Semantic Data Gatherer, a Semantic Network Consistency Checker, an Inference Engine, a Semantic Query Processor, a Natural Language Parser, an Email Knowledge Agent and a I~.nowledge Domain Manager. The server' includes a second server component that hosts domain-specific information that is used to classify and categorize semantic infonnatian. The first and second server components work together and may be physically integrafed or separate.
~ltl7ltl the system, all objects or events in a given hierarchy are active Agents semantically related to each other and representing queries (comprised of underlying action code) that return data objects for presentation to the client according to a predetermined and customizable theme or "Skin." This system provides various means for the client to customize and "blend" Agents and the underlying related queries to optimize the presentation of the resulting information.
The end-to-end system architecture of the present invention provides multiple client access means of communication between diverse knowledge information sources iyia an independent Semantic 5~eb platform or via a traditional Web ,portal (e.g., Today's Veleb access browser) as modified by the present invention providing additional' SDI. layers that enable programmatic integration with a cust~am client.
The methodology of the present invention is directed in part to the operational aspects of the entire system, including the retrieval, management, delivery and presentation of knowledge. This preferably includes securing information from information sources, semantically linking the information from tLie information sources, maintaining the semantic attributes of the body of semantically linked information, delivering requested semantic information based upon user queries and presenting semantic information according to customizable user preferences. Alternative embodiments of the methodology of the present invention are directed to the operation of Agents representing queries that are used with server-side and client-side applications to enable efficient, inferential-based queries producing semantically relevant infonnatian.
BRIEF DESCRIPTION OF THE DRAWINGS
The preferred and alternative embodiments of the present invention are described in detail below with reference to the following; drawings.
FIGURE 1 is a table showing the te~~hnology layers of Today's Web.
FIGURE 2 is a table showing the technology layers of the conceptual Semantic Web.
FIGURE 3 is a diagram showing user navigation to links in Today's Web.
FIGURE 4 is a diagram showing user navigation to links in the conceptual Semantic Web.
FIGURE S is a screenshot showing a sample Information Agent Results Pane 111 accordance with the present invention.
FIGURE 6 shows the technology platform stacks of Today's Web and the Information Nervous System of the present :invention.
1 S FIGURE 7 is a diagram showing an ~averview of the system of the present invention.
FIGURE 8 is a diagram showing the end-to-end system architecture for the Information Nervous System of the present invention.
FIGURE 9 is a diagram showing; the system architecture for the Knowledge Integration Server (KIS) of the Information Nervous System of the present invention.
FIGURE 10 is a comparison between the high-level descriptive platform layers of Today's Web and the equivalents (where applicable) in the Inforn~ation Nervous System of the present invention.
FIGURE 11 illustrates the preferred embodiment of the Information Nervous System and illustrates the heterogeneous, cross-platfann context for the present invention.
FIGURES 12-14 show exemplar screenshots of aspects of the Blender Wizard user interface according to a, preferred embodiment of the present invention.
1s5 FIGURE 15 is an exemplar pane of a Breaking News Agent user interface.
FIGURE 16 illustrates a preferred embodiment showing the Open Agent dialog of the present invention.
FIGURES 17-19 illustrate the Tree View of a sample Semantic Environment involving the Open Agent dialog.
FIGURE 20 shows the Agent schf;ma of the preferred embodiment of the present invention.
FIGURE 21 shows the AgentTypeIDs of the preferred embodiment of the present invention.
FIGURE 22 shows the AgentQueryTypeIDs of the preferred embodiment of the present invention.
FIGURE 23 illustrates sample semantic queries that correspond to Agent names showing how server-side Agents are preferably configured on the ISIS of the present invention.
FIGU~ 24 is a diagram showing an. overview of the ISIS of the present invention.
FIGURE 25 is a diagram showing a sample Semantic Network directed towards an enterprise situation in accordance with the present invention.
FIGURE 26 is a table showing the preferred schema of the Object type in accordance with the present invention.
2p FIGURE 27 shows the SemanticLinks table of the present invention.
FIGURE 28 is a table showing predicate type IDs of the preferred embodiment of the present invention.
FIGURE 29 is a table showing the preferred user object schema made in accordance with the present invention.
FIGURE 30 is a table showing MailingAddressTypeIDs preferably associated with the User (person) object schema.

FIGURE 31 is a table of the preferred category object schema made in accordance with the present invention.
FIGURE 32 is a table of the preferred document object schema made in accordance with. the present invention.
FIGURE 33 shows the Print Media Type IDs of the preferred embodiment.
FIGURE 34 shows the preferred FORMATTYPEID.
FIGURE 35 shows the preferred email message list object schema made in accordance with the present invention.
FIGURES 36 and 37~ are exemplar tables showing the email distribution list and email public folder object schemas, respectively, of a preferred embodiment of the present lIlventl On.
FIGURE 38 shows the preferred Puh~licFolderTypeID of the present invention.
FIGURE 39 shows the preferred event object schema message list object schema made in accordance with the present invention.
FIGURE 4~0 shows the events types of a preferred embodiment of the present invention.
FIGURE 41 shows the preferred media object schema message list object schema made in accordance with the present invention.
FIGURE 42 shows the media types of a preferred embodiment of the present invention.
FIGURES 43-45 illustrate additional samples showing how objects are categorized and utilized in the preferred embodiment of the present invention.
FIGURE 46 is an object graph showing mapping of raw email XML metadata to the Semariti.c Network according to the present invention:
FIGURES 47-53 are exemplar screenshots showing aspects of Agent management by the KIS.
is7 FIGURE 54 shows a sample user interface illustrating an information object displayed in the Information Agent Results Pane.
FIGURE 55 shows an example of a balloon popup associated with an Ilitrinsic Semantic Link showing an email sample according to the present invention.
FIGURE 56 shows an example of a balloon popup associated with a Verb user interface according to the present invention.
FIGURE 57 shows an example of a balloon popup associated with a Deep Information Mode user interface according to the present invention.
FIGURES 58 and 59 are illustraticms showing an exemplar Semantic Environment according to the present invention.
FIGURES 60-68 provide exemplar screenshots of an Information Agent according t~
a preferred embodiment of the present invention.
FIGURES 69-71 provide exemplar balloon popup menus associated with the Smart Lens feature of an Information Agent accoreling to the present invention.
FIGURE 72 shows a sample of a variant of the balloon popup menu of FIGURE 71 showing the relatedness measure of the two objects.
FIGURES 73-75 show sample~table~, illustrating the behaviors and relational contains objects types predicates when using Smart Lenses.
FIGURE 76 is a user interface sample illustrating semantic results PlayerlPreview Control according to the present invention.
FIGURE 77 is a user interface sample showing the semantic results of a Blender.
FIGURES 78 and 79 illustrate exemplar functionality mappings of the present invention.
FIGURE 80 illustrates a user interface showing Agent results and corresponding Context Palettes according to the present invention.
lss FIGURE 81 shows a sample Slnart Recommendations popup context Results Pane according to the present invention.
FIGURE 82 is a table showing the technology layers of the IIIfOrI11at1011 Nervous System of the present invention.
FIGURE 83 illustrates dynamic linking and user-controlled navigation and browsing according to a preferred enlbOdllllellt of the present invention.
DOCUMENTS INCORPORATED BY REFERENCE
The Appendix attached hereto and referenced herein is incorporated by reference.
This Appendix includes exemplar code illustrating a preferred embodiment of the present invention.
C~1~1TENTS OF DETAILED DESCRIPTION OF THE INVENTION
A. DEFINITIONS

B. OVERVIEW

1. INVENTION CONTEXT

1 2. ~a9L UE ~ROhOSITIONS
S

I'~DA1'~S' 6gINFOR~IATIONe9 f~PEE I'S TtIE ZNF~R~fATION1~,L~R6'~IIS
S'3'STErbI OF

TIIE d~RESENT INVENTION

C. SYSTEM ARCHITECTURE AND TECHNOLOGY CONSIDERATIONS

1. SYSTEM ~VERYIEId' 2O 2. SYSTEMARCIIITECTURE

3. TECIINOLOGYSTACKS

4. SYSTEMHETEROGE71'EITY

S. SECURITY

B. EFFICIENCY CONSIDERATIONS

25 D. SYSTEM COMPONENTS AND OPERATION

I. A GENCIES AND t1 GENTS

a. Age~rcies b. Agents Z. ~ KNOIIsLEDGE INTEGRATION SERVER

30 a. Senrarrtic Netlvork b. Senrarrtic Data Gatherer c. Semantic Nehvork Consistency Checker' d. Ir ference Engine e. Semantic Quel y Processor' f. Natztral Langrtage Farser g. Enaail Knowledge Al;ent 77. Knowledge Domain Manager i. Other' Components 3. KNO IVLEDGE BASE SER VER

4. INFORMATION A GENT ~SErI~ANTIC BRO I f~SER PLATFORlIT~

a. Overview b. Client Configuration c. Client Framework Specification d. Client,Framework e. Sellaantic Quel;y Docrrnaent f. Senaaratie Er7V11'~r7l7aelrt g. Sel7aaratlC E71V11'Olt)ne)at lvlanagel' I7. E11V11'011171e1tt Br'OlvSe1' (Se177alatlC Bl'OWSer' OT Ilrf0177aat1017 AgelatT~) i. Additional Application Featztres $. PRO RIDING CONTEXT IN TfIE PRESENT INTENTION

2D a. Context Templates b. Context Skir7s c. Skin Templates d. Default Predicates e. Context Predicates f. Context Attributes g. Context Palettes h. Intrinsic Alerts i. Smart Recommendations G. , PROPERTYBENEFITS OF TIIE.~RESENT INDENTION

E. SCENARIOS

I. EXAMPLES OF SEMANTIC QUERIES UTILIZING THE PRESENT
INVENTION

2. BUSINESS PROBLEMS

3. SITUATIONS

DETAILED DESCRIPTION OF TI-IE INVENTION

A: DEFINITIONS
ActionScript. Scripting language of Macromedia Flash. This two-way communication assists users in creating interactive movies. See http:l! _www.macromedia.comlsupportlflashlaction_scriptslactionscript tutoriall.
. Agency. A named instance of a Knowledge Integration Server (KIS) that is the semantic equivalent of a website.
Agency Directory. A directory that stores metadata information, for Agencies and allows clients to add, remove, search, and browse Agencies stored within.
Agencies can be published on directories like LDAP or the Microsoft Active Directory. Agencies can also be published on a proprietary directory built specifically for Agencies.
Agent. A semantic filter query that returns ~~IL information for a particular semantic object type (e.g., documents, email, people, etc.), context (e.g., Headlines, Conversations, etc.) or Blender.
~ BlenderTM or Compound AgentT"'. Trademarked name for an Agent that 1 ~ Colltalll5 Other Agents and alloys the user (in the case of client-side blenders) or the Agency administrator (in th~~ case of server-side blenders) to create queries that generate results that are the union or intersection of the results of their contained Agents. In the case of client-side blenders, the results can be generated using different views (showing each Agent in the blender in a different frame, showing all the objects of a particular object type across the contained Agents, etc.) ~ Breaking News AgentT"t. Trademarked name for a Smart Agent that users specially tag as being indicative of time-criticality. Users can tag any Smart Agent as a Breaking News Agent. This attribute is then stored in users' Semantic Environment. A Breaking News Agent preferably shows an alert if there is breaking news related to any information being displayed.
~ Default AgentT~l. Trademarked name for standardized, non-user modifiable Agents presented to the user. , ~~ Domain AgentT~'~. Trademarked name for an Agent that belongs to a semantic domain. It is initialized with an Agent query that includes reference to the "categories" table.

~ Dumb AgentT"~. Trademarked name for an Agent that does not have an Agency and which refers to local information (on a local hard drive), on a network share or on a Web link or URL. Dumb Agents are used to essentially load infomlation items (e.g., documents) from a. non-smart sandbox (e.g., the file-system or the Internet) to a smart sandbox (the Information Nervous System via the Inforniation .. Agent (semantic browser)).
~ Email AgentT"z (or Email Knowledge AgentTn~. Trademarked names for a Public Agent used to publish or annotate information and share knowledge on an Agency. ' ~ Favorite AgentT"t. Trademarked name for Agents that users indicate they like and access often.
Public AgentT"t. Trademarked name for Agents that are created and managed by the system administrator.
~ Private or Local AgentsTr~. Trademarked names for Agents that are created and managed by users.
~ Search AgentTr~. Trademarked) name for a Smart Agent that is created by searching the semantic environment with keywords or by searching an existing Smart Agent, in order to invoke an additional, text-based query filter on tle Smart Agent.
o Simple or .Standard AgentT'~t. Trademarked names for Standalone Agents that encapsulate structured, non-semantic queries (e.g., from the local file system or data source).
~ Smart AgentT"f. Trademarked name for a standalone Agent that encapsulates structured, 'semantic queries that refers to an Agency via its XML Web Service.
~ Special AgentT"~. Trademarked name for a Smart Agent that is created based on a Context Template.
Agent Discovery. The property of the information medium of the present invention that allows users to easily, and automatically discover new server-side Agents or client-side Agents created by others (friends or colleagues). Also see "Discoverability."
Annotations. Notes, comments, or explanations that are used to add personal context to an information object. In the preferred embodiment, annotations are email messages that are linked to the object they qualify, and which can have attachments (just like regular email messages). In addition, annotations are first class information objects in the system and as such can be annotated themselves, thereby resulting in threaded annotations or a tree of allnOtatlollS Wlth the initial object as the root.
Application Programming Interlface (API). Defines how software programmers utilize a particular computer feature. Al?Is exist for windowing systems, file systems, database systems, networking systems, and other systems.
Calendar Access Protocol (CAP). Internet protocol that permits users to digitally access a calendar store based on the iCalendar standard.
Compound Agent ManagerT"z. Trademarked name for an Agency component that programmatically allows the user to create and delete Compound Agents and to manage them by adding and deleting Agents.
C~ntext. Information surrounding a particular item that provides meaning and otherwise assists the information consumer in interpreting the item as well as finding other relevant information related to the item.
C~ntext I~csult~ Panc. A Results Pane that displays results for context-based queries.
These include results for Context Palettes, Smart Lenses, Deep Information, etc. See "Results Pane."
Context-Sensitivity. The property of an information medium that enables it to intelligently and dynamically perceive the context of all the information it presents and to present additional, relevant information given that context. A context-sensitive system or medium understands the semantics of the information it presents and provide appropriate behaviors (proactive and reactive based on the user's actions) in order to present information in its proper context (both intrinsically and relationally).
Context Templater"z. Trademarked name. for scenario-driven information query templates that map to specific and familiar semantic models for information access and retrieval. For example,. a "Headlines" template in the preferred embodiment has parameters that are consistent with the delivery of "Hc;adlines" (where freshness and the likelihood of a high interest level are the primary axes for retrieval). An "Upcoming Events"
template has parameters that are consistent with the delivery of "Upcoming Events." And so on.
Essentially, Context Templates can be analogized to personal, digital semantic information retrieval "charu~els" that deliver inforniation to the user by employing a well-known semantic template.
Deep InformationTM. Trademarked name for a feature of the present invention that enables the Information Ageyt to display intrinsic, contextual information relating to an informatiomobject. The contextual information that includes information that is mined from the Semantic Network of the Agency from whence the object came.
Disc~verability. The ability of the information medium of the present invention to intelligently and proactively make information known or visible to the user without the user having to explicitly look for the information.
Domain Agent WizardT"i. Trademarked name for a system component and its user interface for allowing the .~ger~cg~ administrator to create and manage Domain Agents.
D~TNET (.I'~1ET). MicrosoftOO .NET is a set of Microsoft software technologies for connecting information, people, systems, and devices. It enables software integration through the use of XML Web Services: small, discrete, building-block applications that connect to each other, as well as to other, larger applications, via the Internet. .NET-connected software facilitates the creation and integration of XML Web Services. See http://www.microsoft.com/netJdefined/default.asp).
Dynamic LinkingTM. Trademarked name for the ability of the Information Nervous System of the present invention to allow users to link information dynamically, semantically, and at the speed of thought, even if those information items do not contain links themselves.
By virtue of employing smart objects that have intrinsic behavior and using recursive intelligence embedded in the Information Agency's XML Web Service, each node in the Semantic Network is much smarter than a regular link or node on Today's Web or the conceptual Semantic Web. In other words., each node in the Smaut Virtual Network or Web of the present invention can link to other nodes, independent of authoring.
Each node has behavior that can dynamically link to Agencies and Smart Agents via drag and ;drop and smart copy and paste, create links to Agencies in the Semantic,Environment, respond to lens requests from Smart Agents to create new :(inks, include intrinsic alerts that will dynamically create links to context and time-sensitive information on its Agency, include presentation hints for breaking news (wherein the node can automatically link to breaking news Agents in the namespace); form the basis for deep info that can allow the user to find new links, etc. A
user of the present invention is therefore not at the mercy of the author of the metadata. Qnce the user reaches a node in the network, the user has many semantic means of navigating dynamically and automatically-using context, time, relatedness to Smart Agencies and Agents, ete.
Email XML, abject. An information object with the "Email" information object type. The AML object has the "Email" SI~.IdiL schema (which uses XML).
Environment I~rov~ser. See Information Agent.
Favorite Agents ManagerTM. Trademarked name for a system component and user interface element that allows the Agency administrator to manage server-side Favorite Agents.
Flash. Macromedia Flash user interface platform that enables developers and content authors to embed sophisticated graphics and animations in their content. See http://www.macromedia.com/flash.
w Flash MX. Macromedia Flash MX is a text, graphics, and animation design and development environment for creating a broad range of high-impact content and rich applications fore the ~~~Iriternet. See http://www.macromedia.com/software/flash/productinfo/product overview/.

Global Agency DirectaryTM. Trademarked name for an instance of an Agency Directory that runs on the Internet (or other global network). The Global Agency Directory allows users to find, search, arid browse Internet-based Agencies using their Information Agent (directly in their semantic environmf;nt). Also, see "Agency Directory."
HTTP. Hypertext Transfer Protocol (HTTP) is an application-level protocol for distributed, collaborative, hypernledia information systems. It is a generic, stateless, protocol that can be used for many tasks beyond its use for, hypertext, such as name servers and distributed object management systems, through extension of its request methods, error codes and headers. A feature of HTTP is the typing and negotiation of data representation, allowing systems to be built independently of the data being transferred. See httpa/www.w3.orgiProtocols/ and http:l/www.w3.orgJProtocolslSpecs.html.
Inference EngineT'~s. Trademarked name for the methodology of the present invention that observes patterns and data to arrive at relevant and logically sound conclusions by reasoning. Preferably utilizes Inference Rules (a predetermined set of lmuristics) to add 1 ~ semantic links to the Semantic l~Tetwork of the present invention.
Information. A quantitative or qualitative measure of tlle~relevance and intelligence of content or data and which conveys knowledge.
Information AgentT'~t. ,Trademarked name for the semantic client or browser of the present invention that provides context and time-sensitive delivery and presentment of actionable information (or knowledge) from multiple sources, information types, and templates, and which allows dynamic linking of information across various repositories.
Information Nervous SystemTn~" Trademarked name for the dynamic, self authoring, context and time-sensitive information system of the present invention that enables users to intelligently and dynamically link information at the speed of thought, and with context and time-sensitivity, in order to maximize the acquisition and use of knowledge for the task at hand.

Information ObjectT"~ (or Item or Packet). Trademarked name for a unit of information of a particular type and which conveys knowledge in a given context.
Information Object PivotT"t. Trademarked name for an information object that users employ as a navigational pivot to find other relevant infornlation in the same context.
Information Object Type. See Object Type.
Intelligent Agent. Software Agents that act on behalf of the user to find and filter information, negotiate for services, easily automate complex tasks, or collaborate with other software Agents to solve complex problems. By' definition, Intelligent' Agents must be autonomous or, in other words, freely able to execute without user intervention. Additionally, Intelligent Agents must be able to communicate with other software or human Agents and must have the ability to perceive and monitor the environment in which they reside. See http://www.findarticles.com/cf dls/mOFW>=?/7 4/64694222/pl/article.jhtml).
Internet Calendaring and Scheduling (iCalendar). Protocol that enables the deployment of interoperable calendaring and scheduling services for the Internet. The 1 S protocol provides the definition of a common format for openly exchanging calendaring and scheduling information across the Internet.
Internet Message Access Protocol (IMAP). Communications mechanism for mail clients to interact with mail servers, and manipulate mailboxes thereon.
Perhaps the most popular mail access protocol currently is the Post Office Protocol (POP), which also addresses remote mail access needs. IMAP offers a superset of POP features, which allow much more complex interactions and provides for much more efficient access than the POP
model. See http:/lwww-smi.stanford.edu/projects/imap/ml/imap.html.
Intrinsic Semantic LinkT"s. Tradenuarked name for semantic links that are intrinsic to the schema of a particular information object. For instance, an email information object has intrinsic links like "from " "to " "cc " "bcc " and "attachments" that are native to the > > > >
object itself and are defined in the schema for the email information object type.

Island. An information repository that is isolated from other repositories which may contain relevant, semantically related, context and time-sensitive information but which are disconnected from other contexts in which such information might be relevant.
J2EE. The JavaTM 2 Platforni, Enterprise Edition .(J2EE) used for developing mufti-tier enterprise applications. J2EE bases enterprise applications on standardized, modular components by providing a set of services to those components and by handling many details of application behavior automatically. See http://java.sun.com/j2eeloverview.html:
Know~tedge. W formation presented in a context and time-sensitive manner that enables the information consumer to learn from the information and apply the infomlation in order to make smarter and more timely decisions for relevant tasks.
Knowledge AgentTM. See Infonmati~an Agent.
Kno~~~ledge Base ServerT~'i (KBS). Trademarked name for a server that hosts ., knowledge for the ICnovJledge Integration Server (ISIS).
Knowledge l~orraain ManagerT~' (:(~I~'I). 'Trademarked name for a component of the Knowledge Integration Server that is responsible for adding and maintaining domain-specific intelligence on the Semantic Network.
ICnow~ledge Integration ServerT"z (KIS). Trademarked name for a server that semantically integrates data from multiple diverse sources into a Semantic Network, which can also host server-side Agents that provide access to the network and which hosts XML
Web Services that provide context and time-sensitive access to knowledge on the server.
Knowledge WebT"'. See Information Nervous System.
Liberty Alliance. The vision of the :Liberty Alliance is to enable a networked world in which individuals and businesses can more easily conduct transactions while protecting the privacy and security of vital identity information. To accomplish its vision, the Liberty Alliance seeks to establish an open standard for federated network identity through open technical specifications. See http:/lwww.projectliberty.org/index.hhnl.
Lightweight Directory Access Protocol (LDAP). Technology for accessing common directory information. LDAP has been embraced and implemented in most network-oriented middleware. As an open, vendor-neutral standard, LDAP
provides an extendable architecture for centralized storage and management~of information that needs to be available for today's distributed systen-~s and services. LDAP is currently supported in most network operating systems, groupware and even shrink-wrapped network applications.
See http://publib-b.boulder.ibm.com/Redbor~ks.nsf/RedbookAbstracts/sg244986.html?Open.
Link TempIateTM. See Context Template.
Local C~ntex~t. Local Context refers to client-side information objects and Agents accessible to the users. This includes Agents in the Se~i~antic Enviromnent, local files, folders, email items in users' email inbo:Kes, users' favorite and recent Web pages, the current Web page(s), currently opened documents, and other information objects that represent users' current tasl~9 location, time, or condition.
Meaning. The attributes of behavior of information that allows the consumer of the information to locate and navigate to it based on its relevant information content (as opposed to its text or data) and to act on it in a context and time-sensitive manner, in order to maximize the utility of the information.
Metadata. "Data about data." It includes those data fields, links, and attributes that fully describe an information object.
Natural Language Parser. Parsing and interpreting software component that understands natural language queries and can translate them to structured semantic infonrialion queries.

NervanaT"x. Trademarked name for a proprietary, end-to-end implementation of the Inforniation Nervous System information medimn/platfonn. The name also defines a proprietary namespace for resource type and predicate name qualifiers.
.NET Passport. Microsoft .NET Passport is a suite of Web-based services directed towards the Internet and online purchasing. .NET Passport provides users with single sign-in (SSI) and fast purchasing capability at a growing number of participating sites, reducing the amount of information users must remember or retype. .NET Passport provide a high-quality online experience for a large user base and uses powerful encryption technologies-such as Secure Sockets Layer (SSL) and the Triple Data Encryption Standard (3DES) algorithm-for data protection. Privacy is a key priority as well, and all participating sites sign a contract in which they agree to post and follow a privacy policy that adheres to industry-accepted guidelines.
Nehvork Effects. This exists when 'the number of other users affects the value of a product or service to a particular user. Telephone SerV~ce provides a clear example. The value of telephone service to users is a function of the number of other subscribers. Few would be interested in telephones that were not connected to anyone, and, most would assess higher value to a phone service linked to a national network rather than just a local network.
Similarly, many computer users prize a computer system that allows them to exchange information readily with other users.
Network Effects are thus demand-sidw externalities that generate a positive feedback effect in which successful products become more successful. In this way, Network Effects are analogous to supply-side economies of scale and scope. As a firm increases output, economies of scale lead to lower average costs, permitting the fine to lower prices and gain additional business from rivals. Continued expansion results in even lower average costs, justifying even lower prices. Similarly, the positive feedback from Network Effects builds upon previous successes. In the computer industry, for example, users pay more for a more popular computer system, all else equal, or opt for a system with a larger installed base if the prices and other features of two competing systems are equivalent. See http:l/www.ei.com/publications/199G/fall l .htm.
Network News Transfer Protocol (NNTP). Protocol for the distribution,':
inquiry, S retrieval, , and posting of news articles using a reliable stream~based transmission of news among the ARPA-Internet community. NNTP is designed so that news articles are stored in a central database allowing subscribers to select only those items they wish to read. Indexing, cross-referencing, and expiration of aged m~°ssages are also provided.
Notifications. Notifications are alerts that are sent by the Information Agent or an Agency to indicate to a user that there is new information on an Agent (either a client-side Agent or a server-side Agent). Users can request notifications from Agents in their Semantic Environment. Users can indicate that they have received the notification. The notification source (the client or server) stores infornlat:ion for the user and the Agent indicating the last time the user acknowledged a notification for the Agent. The notification source polls the Agent to check if there is new infornzation since the last acknowledge time.
If there is, the notification source alerts the user. Alerts can be sent via email, pager, voice, or a custom alert mechanism such as Microsoft's .NET Alerts service. Users have the option of indicating their preferred notification mechanism for the entire notification source (client or server)-which applies to all Agents on the notification source-on a per-Agent basis (which overrides the indicated preference on the notification source.
Object. See Information Object.
Object Type. Identification data associated with inforn~ation that allows the consumer to understand the nature of the information, to interpret its contents, to predict how the information can be acted upon, and to link it to other relevant information items based on how the object types typically relate in the real world. Examples include documents, events, email messages, people, etc.

Ontology. Hierarchical structuring of lmowledge according to essential qualities.
Ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, where "Ontology" is a systematic account of Existence. For artificial intelligence systems, what ''exists" is that which can be represented. When the,knowledge of a domain is represented in a declarative formalism, the set of objects that can be represented is called the universe of discourse. This set of objects, and the describable relationships among them, are reflected in the representational vocabulary with which a knowledge-based program represents knowledge. Thus, in the coni:ext of artificial intelligence, the ontology of a program is described by defining a set of representational terms. In such ontology, definitions associate the names of entities in the universe of discourse (e.g., classes, relations, functions, or other objects) with human-readable te:~t describing what the names mean, and formal axioms that constrain the interpretation and well-forn~ed use of these terms.
Formally, ontology is the statement of a logical theory.
The subject of ontology is the study of the categories of things that exist or may exist 111 Some damain. The product of such a study, called ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose ~of talking about D. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D. See, generally, http:/lwww-ksl.stanford.eduikstiwhat-is-an-ontology.html and http:llusers.bestweb.nethsowa/ontologyl).
Predicates. A Predicate is an attribute or link whose result represents the truth or.
falsehood of some condition. For example, the predicate "authored by" links a person with an information object and indicates whether a person authored the object.
PresenterTni. System component in i:he Information Agent (semantic browser) of the present invention that handles the aggregation and presentation of results from the semantic query processor (that preferably interprets SQML). The Presenter handles layout management, aggregation, navigation, Skin management, the presentation of Context Palettes, interactivity, animations, etc.
RDF. Resource Description Framework (RDF) is a foundation for processing metadata; it provides interoperability between applications that exchange S machine-understandable information on the Web. RDF emphasizes facilities to enable automated processing of Web resources. RDF defines a simple model for describing relationships among resources in terms of named properties and values. RDF
properties may be thOllght of as attributes of resources and in this sense correspond to traditional attribute-value pairs. RDF properties also represent relationships between resources. As such, the RDF
data model can therefore resemble an entity-relationship diagram.
RDF can be used in a variety of application areas including, for example: in resource discovery to provide better search engine capabilities, in cataloging for describing the content and content relationships available at a particular Web site, page, or digital library, by intelligent software Agents to facilitate knowledge sharing and exchange, in content rating, in describing collections of pages that represent a single logical "document', for describing intellectual property rights of Web pages, and for expressing the privacy preferences of a user as well as the privacy policies of a Web site. RDF with digital signatures is preferably a component of building the "Web of Trust" for electronic commerce, collaboration, and other applications. See, generally, http://www.w3.org/TR/PR-rdf syntax! and http://www.w3.org/TR/rdf schema/.
RDFS. Acronym for RDF Schema. Resource description communities require the ability to say certain things about certain kinds of resources. For describing bibliographic resources, for example, descriptive attributes including "author", "title", and "subject" are common. For digital certification, attributes such as "checksum" and "authorization" are often .required. The declaration .of these properties (attributes) arid their corresponding semantics are defined in the context of RDF as an RDF schema. A schema defines not only .. , the properties of the resource (e.g., title, author, subject, size, color, etc.) but may also deftne tile kinds of resources being described (books, Web pages, people, companies, etc.). See http:lJwww.w3.org/TRlrdf schema/).
Results PaneTn~. Trademarked name for the graphical display area within the Infonnation Agent (semantic browser} that displays results of an SQML query.
See FIGURE 5, showing a sample Infornlation Agent screenshot illustrating server-side Agents, an optional player control/navigationlfilter toolbar, a "Server-Side Agents Dialog" (which allows users to browse and open server-side Agents), and sample results (with the "Documents" information object type) from a server-side Agent.
Semantics. Connotative meaning.
Semantic Envir~nmentT~g. 'This refers to all the data stored on users' local machines, in addition to user-specific data on an Agency server (e.g., subscribed server-side Agencies, server~side Favorite Agents, etc.). Client-side state includes favorite and recent Agents and authentication and authorization information (e.go, user names and pass~.a~or°ds fear carious Agencies), in addition to the SQML files and buffers for each client-side (user-created) Agent. The Information Agent is preferably configured to store Agents for a set amount of time before automatically deleting them, except those that have been added to the "favorites"
list. For example, users may configure the Information Agent to store Agents for two weeks.
In this case, Agents older than two weeks a:re automatically purged from the system and the Semantic Environment is adjusted accordin;;ly. The Semantic Environment is employed for Context Palettes (Context Palettes use the Agencies in the "recent" and "favorites" list in order to predict what default Agencies users want to view context from).
Semantic Environment ManagerT"z. Trademarked name for a software component that manages all the local state for the Semantic Environment (in the Information Agent).
This includes storing and managing the metadata for all the client-side Agents (and the history and favorites Agent sub-lists), per-A gent state (e.g., Agent Skins, Agent preferences, etc.), notification management, Agency browsing (on Agency directories), listening for Agencies via multicast and peer-to-peer anr-rouncement protocols, services to allow users to browse the Semantic Environment via the semantic browser (via the Tree View, the "Open Agent" dialog, and the Results Pane), etc.
Semantic Data GatUererTr~ (SDG). Trademarked name for XML Web Service used by the Knowledge Integration Server (KISS) and which is responsible for' adding, removing and updating entries in the Semantic Network via the Semantic Metadata Store (SMS).
Semantic Metadata StoreTn~ (SM;i). Trademarked name for a software component on the KIS that employs a database (e.g., SQL Server, Oracle, DB2) having tables for each primary object type to store all the metadata. on the KIS.
Semantic I~etvmork. System and method of linking objects associated with schemes together in a semantic way via the database tables on the Semantic Metadata Store.
Semantic Network Consistency CheckerT"'. Trademarked name for a software component that runs on an Agency of the present invention that is tasked with maintaining the integr ity and consistency of the Semantic Network. The checker runs periodically and ensures that entries in the "SemanticLinks" table exist in the native object tables, that entries in the "objects" table exist in the native object tables and that all entries in the Semantic Metadata Store still exist at the repositories i:rom where they were gathered.
Semantic Queries. Queries that incorporate meaning, context, time-sensitivity, context-templates, and richness that approach natural language. Much more powerful than simple, keyword-based queries in that they are context and time-sensitive and incorporate meaning or semantics.
Semantic Query Markup Language (SQML). A proprietary XML-based query language used by this invention to define, ,tore, interpret and execute client-side semantic queries. SQML includes tags to define a query that gets its data from diverse resources (that represent data sources) such as files, folders, application repositories, and references to 2os Agency XML Web Services (via resource identifiers and URLs). In addition, SQML
includes tags that enable semantic filtering (via custom links and predicates) which indicate how data is to be queried and filtered from the resources, and arguments that indicate how the resources are to be queried and how the results are to be filtered. In particular, the arguments can include references to local or remote context. The context arguments are then resolved by the client-side SQP at run-time to XML nnetadata. The XML metadata is then passed to the appropriate resource (e.g., an Agency's XIvIL Web Service) as a method call along with the reference to the resource and the semantic links and predicates that indicate how the query is to be resolved by the resource (e.g., the Agency's XML Web Service). SQML is to the Information Nervous System as HTML is to Today's Web. The main difference is that SQML defines the rules for semantic querying while HTML defines the rules for Hypertext presentation. However, SQML is superior in that it enables the client to recursively create new semantic queries from existing ones (by creating new SQML with new links derived from an existing SQML query), e.g., via drag and drop and smart copy and paste, the Smart Lens, Context Templates and Palettes, etc. In addition, because SQML does not define the rules for presentation, the results of the semantic query can be presented in multiple ways, using a "skin" that takes the results (in Sl~I'vIL) to generate presentation based on the user's preferences, interests, condition, or context.. Furthermore, SQML can contain abstract links and predicates such as those that refer to or employ Context Templates. The resource (e.g., the Agency's XML Web Service) then resolves the SQML to an appropriate query format (e.g., SQL or the equivalent in the case of an Agency's XML Web Service) and then invokes the "actual" query in order to generate the results (which will then account for the user's .
context or Context Template). Also, an SQML buffer or file can refer to multiple resources (and Agencies), thereby empowering the client to view results in an aggregated fashion (e.g., ~ based on context. or time-sensitivity), rather than based on the source of the data - this is a powerful feature of the invention that enables user-controlled browsing and information aggregation (see the sections on both below). Lastly, every client-side Agent has an SQML
definition and file, just as every Web page has an HTML file.
Semantic Query ProcessorT"i (SQP). Trademarked name fox the server-side semantic query processor (XML Web Service in the preferred embodiment) that takes SQML
and converts it to~ SQ,L (in the preferred embodiment) and then returns the -results as XML.
On the Ka~owledge Integration Server (KIS), the SQP is the main entry point to the Semantic Network of the present invention responsible for responding to semantic queries from clients of the KIS. On the server, this is the software component that processes semantic queries represented as SQML from the client. On the client, the client-side SQP takes aggregate SQML and compiles or maps it to individual SQML queries that can be sent to a server (or ' Agency) XML Web Service.
Semantic Results lVTarlcup Language (SRML). A proprietary XML-based data schema and format used by this invention. to define, store, interpret and present semantic results. On the client, SR1VIL is returned from the SQP via semantic resource handlers that I.ea interpret, format, and issue query requests to semantic data sources.
Semantic data sources will include an Agency's XML Web Sen~ice, local files, local folders, custom data sources from local or remote applications (e.g., a lvlicrosoft Outlook email application inbox), etc.
The XML Web Service will return SRML to a client, in response to the client's semantic query. This way, the XML Web Service will not "care" how the results are being presented at the client. This is in contrast with Today's'Neb and the Semantic Web where servers return already-formatted HTML for a client to present and where clients merely present presentation data {as opposed to semantic data) and camlot customize the presentation of the data. In .this invention, two clients can render the same SRML in completely differeyt ways, based on the current "skin" that has been selected or applied by the user of either client. The "skin" then converts " the SRML to a presentation-ready format sucli~ as XHTML, DHTML+TIME, SVG, Flash MX, etc.

SRML is a meta-schema, meaning that it is a container format that can include data for different ~infonnation object types (e.g., documents, email, people, events, etc.). An SRML file or buffer can contain intertwined results for each of these object types.
Well-formed SRML will contain well-formed XML document sections that are consistent with the schema of the information object types that are contained in the semantic result the SRML represents. See Sample A of the Appendix hereto.
Semantic Web. Extension of 'Today's Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. See Tim Berners-Lee, James Hendler, Ora Las:>ila, The Sema»tic l~'eb, Scie»tific A»aerica», May X000.
Facilities to put machine-understandable data on Today's Web axe becoming a high priority for many communities. The Web can reach its full potential only if it becomes a place where data can be shared and processed by automated tools as well as by people. For the Web to scale, tomorrow's programs must be able to share and process data e~rer~ when these programs have been designed totally independently. The Semantic Web is a conceptual vision: the idea of having data on the Web defined and linked in a way that~it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications. See also httpa/www.rv3.org/2001/sw/.
Session Announcement Protocol (SAP). In order to assist the advertisement of multicast multimedia conferences and other multicast sessions, and to communicate the relevant session setup information to prospective participants, a distributed session directory may be used. An instance of such a session directory periodically multicasts packets containing a description of the session, and these advertisements are received by other session directories such that potential remote participants can use the session description to start the tools required to participate in the session.
2os In its simplest form, this involved periodically multicasting a session announcement packet describing a particular session. To receive SAP, a receiver simply listens on a well-known multicast address and port. Sessions are described using the Session Description Protocol (ftp:/lftp.isi.edu/in-notes/rfc2327.txt). If a receiver receives a session amouncement packet it simply decodes the SDP message, and then can display the session information for the user. The interval between repeats of the same session description message depends on the number of sessions being announced (e:ach sender at a particular scope can hear the other senders in the,same scope) such that the bandwidth being used for session announcements of a particular scope is kept approximately constant. If a receiver has been listening for a set time, and fails to hear a session announcement, then the receiver can conclude that the session has been deleted and no longer ~;xists. The set period is based on the receivers' estimate of how often the sender should be sending.
See, generally, http://www.faqs.orglrfcs/rfc2974.html, http:/lwww.video.ja.net/mice/archive/sdr docs/nodel.html, ftp://ftp.isi.edu/in-notes/rfc2327.txt.
Simple Mail TransFer FrOtocol (SlYITP). Protocol designed to transfer mail reliably and efficiently. SMTP is independent of the particular transmission subsystem and requires only a reliable ordered data stream channel. An important feature of SMTP is its capability to relay mail across transport environments. See http://vvww.ietf.org/rfc/rfc0821.txt.
Skins. Presentation templates that are used to customize the user experience on a per-Agent basis or which customizes the presentation of the entire layout (independent of the Agent), or object (based on the information object type), context (based on the Context Template), Blender (for Agents that are Blenders), for the semantic domain name/path or ontology, and other considerations. Each Agent will include a Skin which in turn will have an xML, metadata representation of parameters to customize the layout of the XML~'results that represent information objects (the layout Skin), for example;~whether or not those results are animated, the manner in which each result is displayed, including a representation of the object type (the object Skin), styles, colors, graphics, filters, transforms, effects, animations (and so on) that indicate the ontology of the current results (the ontology Skin), styles that indicate the Context Template of the current results (the context Skin) and styles that indicate how to view and navigate results from Blenders (i.e., the Blender Skin).
Smart LensT'~t. Trademarked name for a proprietary feature of this invention that allows users to select a Smart Agent or an object as a context with which to view another .
object or Agent. The lens then displays met~adata, links, and result previews that give users an indication of what they should expect if the context is invoked. Essentially, the Smart Lens displays the results of a "potential query." The Smart Lens allows users to quickly preview context results without actually invoking p,ueries (thereby increasing their productivity). In addition, the Smart Lens can display views that are consistent with the context, using pivots, templates and preview windows, thereby allowing~users to analyze the context in different ways before invoking a query.
Smart virtual- WebT~'z. Trademarke°.d name for the property of the present invention to integrate semantics, context-sensitivity, time-sensitivity, and dynamism in order to empower users to browse a dynamic, virtual, "on-the-fly," user-controlled "Web" that they control and can customize. This is in contrast with Today's Web and the conceptual Semantic Web, both of which employ a manually authored network wherein users are at the mercy of the authors of.the information on the network.
Structured Query Language (SQL). Pronounced "ess-que-el." SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. SQL
statements are used to perform tasks such as update data on a database, or retrieve data from a database.
Some common relational database management systems that use SQL are: Oracle, Sybase, Microsoft SQL Server, Access, Ingres, etc. .Although most database systems use SQL, most of them also have their own additional proprietary extensions that are usually only used on their system. However,, the standard SQL commands such as "Select", "Insert", "Update", "Delete", "Create", and "Drop" can be used to accomplish almost everything that one needs to do.with a database. .
SQL works with relational databases. A relational database stores data in tables (relations).. A' database is a collection of tables. A table consists of a listv. of records, each record in a table preferably includes the same structure, and each has a fixed number of "fields" of a given type.
See, generally, http:llwww.sqlcourse.comlintro.html and http:/lwww.dcs.napier.ac.ukhandrew/sql/0lw.htm.
Scalable 'Vector Graphics (SVh). Language for describing two-dimensional graphics in AML. SVG allows for three types of graphic objects: vector graphic shapes (e.g., paths consisting of straight lines and cur<~es), images and text. Graphical objects can be grouped, styled, transformed and composited into previously rendered objects.
Text can be in 1 S any ~I~1IL namespace suitable to the application, which enhances searchability and accessibility of the SVG graphics. The feature set includes nested transformations, clipping paths, alpha masks, filter ,effects, template objects and extensibility. SVG
drawings can be dynamic and interactive. The Document Object Model (DOM) for SVG, which includes the full AML DOM, allows far straightforward and efficient vector graphics animation via scripting. A rich set of event handlers such as onmouseover and onclick can be assigned to any SVG graphical object. Because of ita compatibility and leveraging of other Web standards, features like scripting can be done on SVG elements and other XML
elements from different namespaces simultaneously within the same Web page. See http:l/vww.w3.org/Graphics/SVG/Overview.htm8.
Ta~onotny. An organizational structure wherein divisions .are ordered into groups or categories.

Time-Sensitivity. Property of an infornxation medium to deliver and present information based on when the information would be most relevant in time. For instance, freslxness is an attribute that denotes time-sensitivity. In addition, the delivery and presentation of upcoming events (which, by definition, are time-sensitive) and the nxanner in which the time-criticality of the events are displayed are properties of a time-sensitive medium.
Today's Web. This refers to the World Wide Web as we know it today. Today's Web is a universe of hypertext servers (HT'TP servers), which are the servers that allow text, graphics, sound files, etc. to be linked together. Hypertext is simply a non-linear way of presenting information. Rather than reading or learning about things in the order that an author, or editor, or publisher sets out for u;~, readers of lxypertext nxay follow tlxeir own path, create their own order or meaning out the material. This is accomplished by creating "links"
between information. These links axe provided so that user may "jump" to furtlxer information about a specific topic being discussed (whiclx nxay have more links, leading each reader off into a different direction). The Hypertext medium can incorporate pictures; sound, and video present a multimedia approach to presenting information, also referred to as, hypermedia. See, generally, ~ http://www.w3.org/History.html and http:llwww.umassd.edu/Public/People/KAnxaral/Thesis/hypertext.html.
Multicast Time to Live (TTL). Multicast routing protocol uses the field of datagrams to decide how "far" from a sending host a given multicast packet should be forwarded. The default TTL for multicast datagrams is 1, which will result in multicast packets going only to other hosts on tlxe local network. A setsockopt(2) call may be used to change the TTL. As the value for TTL increases, routers will expand the number of hops they will forward a multicast packet. To provide meaningful scope control, multicast routers typically enforce the following "thresholds" on forwarding based on the T'TL
field:
~ 0 restricted to the same host ~ 1 restricted to the same subnet ~ 32 restricted to the same site ~ G4 restricted to the same region ~ 128 restricted to the same continent ~~ 255 unrestricted See http:/lwww.isl.org/projects/eies/mboneJmbone27.htm.
User State. This refers to all state that is either created by a user or which is needed to cache a user's preferences, favorites, or other personal information on a client or server.
Client-side User State includes authentication credential information, users' Agent lists (and all the metadata including the SQML quE:ries for the Agents), home Agent, configuration options, preferences such as Skins, etc. Essentially, client-side User State is a persisted form of users' Semantic Environment. Server-side User State includes information such as users' Favorite Agents, subscribed Agents, Default Agent, semantic links to information objects on the server (e.g., "favorites" links) etc. SE;rver-side User ~ State is optional for servers but support far it is preferred. Servers preferably support user logon and a "people" object type (even ~~rithout server-side P~gentsj because these are needed for features such as favorites, recommendations, and for Context Tf;mplates such as "Newsmakers," "Experts,"
"Recommendations," "Favorites," and "ClaliSlCS."
Virtual Information Object TypeT°s. Trademarked name for object types that do not map to distinct object types, yet are semantically of interest to users.
Virtual ParameterT"s. Trademarked name for variables, parameters, arguments, or names that are dynamically interpreted at runtime by the semantic query processor. This allows the Agency administrator to store Agents that refer to virtual names and then have those..names be converted to actual relevant terms when the query is invoked.
~ Web of Trust. Term coined by members of the Semantic Web research community that refers to a chain of authorization that users of the Semantic Web 'can use to validate assertions and statements. Based on work in mathematics and cryptography, digital signatures provide proof that a certain person wrote (or agrees with) a document or statement. Users can preferably digitally sign all of their RDF statements.
That way, users can be sure that they wrote them (or at least vouch fox their authenticity).
Users simply tell the program whose signatures to trust. Each can set their own levels of trust (or paranoia), and the computer can decide ho~v much of what it reads to believe.
By way of example, with a Web of Trust, a user can tell a computer that he or she trusts his or her best friend, Robert. Robert happens to be a rather popular guy on the Net, and trusts quite a number of people. All the people he trusts in turn trust another set of people. Each of these measures of trust is to a certain degree (Robert can trust Wendy a whole lot, but Sally only a little). In addition to trust, levels of distrust can be factored in. If a user's computer discovers a docmnent which no one explicitly trusts, but no one has said it has totally false either, it will probably trust that information a little more than one which many people have said is false. The computer takes all these factors into account when deciding the trustworthy of a piece of infomlation. Preferabl~!~ the computer combines all this information into a simple display (tlmmbs-u:p ! thumbs-down) or a more complex explanation (a description of all the various trust factors in'volved). See http://blogspace.comlrdflSwartzHendler.
Web Services-Interoperability (W;3-I). An open industry organization chartered to promote Web services interoperability across platforms, operating systems, and programming languages. The organization works across the industry and standards organizations to respond to user needs by providing guidance, best practices, and resources for developing Web services solutions. See http:/lwww.ws-i.org.
Web Services Security (WS-Security). Enhancements to SOAP messaging providing quality of protection through message integrity, message confidentiality, and single message authentication. These mechanisms can be used to accommodate a wide variety of security models and encryption technologies. WS-Security also provides a general-purpose 111echa111S1n for associating security tOkellS Wlth meSSages. No specific type of security token is required by WS-Security. It is designed to be extensible (e.g. support multiple security token formats). For example, a client might provide proof of identity and proof that they have a particular business certification. Additionally, WS-Security describes how to encode binary security tokens. Specifically, the specification describes how to encode X.509 certificates and Kerberos tickets as well as how to include opaque encrypted keys. It also includes extensibility mechanisms that can be used to further describe the characteristics of the credentials ' that are included with a message. See httpa/msdn.microsoft.com/library/default.asp?url=/librarylen-usldnglobspeclhtml/ws-security.asp.
lExtensible l~arkaap Language (~MF~). Universal fomlat for structured documents and data on the Web. Structured data includes things like spreadsheets, address books, configuration parameters, financial transactions, and technical drawings. XML
is a set of rules (you may also think of them as guidE;lines or conventions} for designing text formats 1 ~ that let you structure your data. XML is not a programming language, and one does not have to be a programmer to use it or learn it. XML makes it easy for a computer to generate data, read data, and ensure that the data structure is unambiguous. XML avoids common pitfalls in language design: it is extensible, platform-independent, and it supports internationalization and localization. XML is fully Unicode-compliant. See ?0 http://www.w3.orglXML/1999/XML-in-10-points.
XML Web Service (also known as "Web Service"). Service providing a standard means of communication .among different software applications involved in presenting dynamic context-driven information to the user. More specific definitions include:
..,1. A software application identified by a URI whose interfaces and binding are 25 capable of being defined, described and discovered by XML artifacts.
Supports direct interactions with other software applications using XML based messages via Internet-based protocols.

2. An application delivered as a service that can be integrated with other Web Services using Internet standards. It is an URL-addressable resource that programmatically returns information to clients that want to use it. The major communication protocol used is the Simple Object Access Protocol (SOAP), S which in most cases is XML over HTTP.
3. Programmable application logic. accessible using standard Internet protocols. Web Services combine aspects of component-based development and the Web. Like components, Web Services represent black-box functionality that can.be reused without worrying about how the service is implemented. Unlike current component technologies, Web Services are not accessed via object-model-specific protocols, such as DCOM, RMI, or IIOP. Instead, Web Services are accessed via ubiquitous Web protocols (ex: FITTP) and data formats (ex: XML).
See http:/lwww.xmlwebservices.cc/, http://www.perfectxml.com/WebSvcl.asp and http://www.w3.org/2002/ws/archi2i06iwd-wsa-rags-20020605.html.
XQuery. (query language that uses the structure of XML to intelligently express queries across all these kinds of data, whether physically stored in XML or viewed as XML
..
via riliddleware. See http://www.w3.org/TR/xquery/ and http://www-106.ibm.com/developerworks/xml/library/x-xquery.htrnl.
XPath. The result of an effort to~ provide a common syntax and semantics for functionality shared between XSL Transformations (http://www.w3.org/TR/XSLT) and XPointer (http://www.w3.org/TR/xpath#XPTR). The primary purpose of XPath is to address parts of an XML [XML] document. In.support of this primary purpose, it also provides basic facilities for manipulation of strings, numbers and Booleans. XPath uses a compact, non-XML syntax to facilitate use of XPath within URIs and XML attribute values.
XPath operates on the abstract, logical structure of an XML document, rather than its surface syntax. XPath gets its name from its use of a path notation as in URLs for navigating through the hierarchical structure of an XML document.
In addition to its use for addressing, XPath is also designed so that it has a natural subset that can be used for matching (testing whether or not a node matches a pattern); this use of XPath is described in XSLT. XPath models an XML document as a tree of nodes.
There are different types of nodes, including element nodes, attribute nodes and text nodes.
XPath defines a way to compute a string-value for each type of node. Some types of nodes also .. have names. ' XPath fully , supports XML Naniespaces (http:/lwww.w3.org/TR/xpath#XMLNAMES). Thus, the name of a node is modeled'~as a pair consisting of a local part and a possibly null namespace ~URI; this is called an (http:l/www.w 3.orglTRlxpath#dt-expanded-name). See http://www~w3.org/TRlxpath#XPTR.
XSL. A style sheet language for XML that includes an XML vocabulary for specifying formatting. See http://www.w3.org/TRlxsltl.l/.
XSLT. Used by XSL to describe how a document is transformed into another XML
document that uses the formatting vocabulary. See http://www.w3.org/TR/xsltl 1/.
B. l7vERVtEw I. INVENTION CONTEXT
There is a misconception that the I~oly Grail for information access is the provision of natural language searching capability. Prior technologies for infornlation access have focused principally on improving the interface for sf;arching for or accessing information to optimize information retrieval. The presumption has largely been that providing a natural language interface to information will perfectly solve users' information access problems and end the frustration users have with f nding information.
In truth, however,, many axes of analysis are involved in how people acquire knowledge in the real world. One example is context. There are many things people know only because of where they were at a certain place and time. If they were not at that place at that time, they would not know what is in fact known or, indeed, nmght not care to know.
Having'the ability to search for what is presently known with natural language does not assist in uncover=ing the knowledge related to that particular time and..place. There are simply no natural parari'leters that~form the correct query to retrieve the desired informatioil.

The conundrum is that a person cannot ask for what he or she might not even know would have value until after the fact. Stated differently, one cannot query for what they do not know they do not know, or for what they do not know that they might want to know.
Context-sensitivity, time-sensitivity, discovery, dynamic linking, user-controlled browsing, users' "Semantic Environment," flexible presentation, Context Skins, context attributes, Context Palettes (which bring up relevant, context and time-sensitive information based on Context Templates) and other aspects of this invention recognize and correct this fundamental deficiency with existing information systems.
For example, people may have many CDs in their library (thereby adding to the "knowledge" of music) because they attended certain parties and spoke with certain people.
Those people at those parties mentioned the CDs to the person, thereby increasing the person's knowledge of music. As another example, a person may purchase a book (if read, increasing the person's knowledge on the particular topic of the book), based on a recommendation from a hitherto unknown stranger the person happened to sit beside on an airplane flight. In the real world, people acquire knowledge based not just on what they read and search for, but also based on the friends they keep, the people with whom they interact and the people whose judgment they trust. The "knowledge environment" is arguably as critical if not more critical for knowledge dissemination and acquisition as the model for retrieval (whether digital or analog).
2Q The present invention mirrors virtually every real-world knowledge-acquisition scenario in the digital world. The resulting Information Nervous SystemTM is the medium doing most of the work but the scenarios map very cleanly to the analog (real) world. The inability of efforts such as natural-language search techniques of Today's Web as well as the Semantic Web to recognize the many ways in which knowledge is disseminated and acquired render them ultimately ineffective. The present invention accounts for the variety of ways in 21s which humans have always acquired knowledge-independent of the actual technology used for information delivery.
By way of example, there has always been context and there has always been time.
Likewise there has always been the notion of discovery and the need to link information dynamically and with user control. There have always been certain Context,Templates, albeit in different mediums that presented herein, including "classics," "history,"
"timelines,"
"upcoming events," "headlines." These templates existed before the creation of the Internet, Today's Web, Email, e-Learning, etc. Nevertheless, prior to the present invention, there was no ability in the electronic medium to focus on the mode, protocol and presentation of knowledge delivery which maps to real-world scenarios (for example, via Context Templates, context-sensitivity, time-sensitivity, dynamic linking, flexible presentation, Context Skins, context attributes, etc.) as opposed to actual information types, semantic links, metadata, etc. There will always be new information types. But the dissemination and acquisition axes of knowledge (e.g.,~Context Templates) have always and vrill always remain the same. The present invention captures this reality.
In addition, the present invention provides the ability to disseminate knowledge via serendipity. Serendipity plays a large part in knowledge acquisition in the real world and it is a first-class mode of knowledge delivery. 'the present invention enables a user to acquire information serendipitously (albeit intelligE:ntly) by its support for context, time, Context Templates, etc.
Information models or mediums that employ a strict, static structure like a "Web"
break down because they assume the presence of an authored "network" or "Web"
and fail to accaurlt for the various axes of knowledge formation. Such information models are not user-focused, do not incorporate context, time, dynamism and templates, and do not map to real-world knowledge . acquisition and dissemination scenarios. The present invention minimizes information loss and maximizes inforn~ation retained, even without the presence of a "Web" per se, and even if no natural language is employed to find information. This is possible because, unlike existing mediums for information access, a preferred embodiment of the present invention focuses on the knowledge dissemination models that incorporate context, time, dynamism, and' templates (for the benefit of both the end-user and the content producer) and not on the specifics of the access interface, or the linking (semantic or non-semantic) of information resources based on static data models or human-based authoring. In many scenarios, a "Web" (semantic or non.-semantic) is necessary as a means of navigation, but is far from being sufficient as a means of knowledge dissemination and acquisition. The Information Nervous System of the pm;sent invention incorporates "knowledge axes"
described in the invention (including but not limited to link-based navigation) and intelligently and seamlessly integrates therr~ to facilitate the dissemination and acquisition of knowledge and to benefit all parties involved in the transfer of knowledge.
2. j~ALUE hROPOSITIONS
Today, knowledge must be "manually hard-coded" into the digital fabric of an 1 S information structure, whether it be for an enterprise, a consumer or the general inquiring , population. If it is not authored and distributed properly, no one knows. of its existence, knows how it relates to other sources of intelligence, or knows how to act on it in real-time and in the proper fashion. This is largely because Today's Web was not designed to be a platfori for knowledge. It was designed to be a platform for presentation and is intentionally dumb, static, and reactive. Today, knowledge-workers-those who seek to use information by adding context and meaning-are at the mercy of knowledge-authors.
A significant aspect of knowledge interaction is to have knowledge-workers be able to navigate their way through a knowledge space in a very intuitive manner, and at the speed at which they wish to make decisions and act on the knowledge. In other words;
knowledge-workers do not have to "think" about an e-Learning island as being separate from documents in their organizations, e-mail that contains customer feedback, media files, upcoming video-conferences, a meeting they had recently, information stored in newsgroups, or related books. The preferred situation is to relegate the information "type" and "source"
and to create a "seamless knowledge experience" that cuts across all those islands in a semantic way.
In creating a knowledge experience, it is also preferred to be able to integrate knowledge assets across content-provider, partner, supplier, customer and people boundaries.
In the enterprise scenario, for example, no single organization has all the knowledge it needs to remain competitive. Knowledge is stored in industry reports, research documents from consulting firms and investment banks, media companies like ReutersTns and BloombergTht, etc. All this constitutes "knowledge." It is not enough to deploy an e-Learning repository to train users on a one-time or periodic basis. ~CJsers should have always-on access to knowledge from a variety of sources, in-place, and in an intelligent context that is relevant to their current task.
All this requires a layer of intelligence and pro-acti~yity that is not a~,railable todaye Today, for example, enterprises use information portals, such as intranets and the Internet, as a way of disseminating information to their employees. However, this is far from being enough, as it provides only presentation-level integration. This is akin to subscribing to newsletters to keep updated with information, as opposed to having an Agent that manages your information for you, helps you discover new information on-the-fly, helps you capture and share information with colleagues, etc.
To accomplish the desired level of knowledge interaction requires Agents working in the background, reasoning, learning, inferring, matching users together based on their profiles, capturing new knowledge and automatically deducing new knowledge, and federating knowledge from external sourcca so that they become a seamless part of the knowledge experience. This in turn requires the semantic integration of knowledge assets so that they all make sense ~in a holistic fashion, rather than mexely providing the basis for presentation-level integration and document searching. The implementation framework and resulting medium must provide real-time, agile discovery and recommendation services so that context and time-sensitive information is "honored" and such that knowledge-workers can.be more productive and get more done faster and withlless. And lastly, the system must work with existing information sources in a plug-n-play manner, must seamlessly and automatically classify and integrate kIIOWIl knowledge assets, and must embed the knowledge tools in the knowledge themselves, thereby adding another "dimension" into knowledge assets.
The present invention is designed to be an intelligent, proactive, real-time knowledge platform that co-exists with Today's Web (or any other layer of presentation).
Incorporation and use of the present invention will allow knowledge-workers to be in control of their knowledge experiences because authoring (via "connections") will be done intelligently, dynamically, automatically, and at the speed. of thought.
3. T°~I)~l ~''~ "ZIVF~RA~ATI~IV" l~i~~'1~ I%S. Tl~E ~I~F~le~T~1 Z'~~ly ~~'If 6~~(I~' ~~'~ST~'~2 ~~°
1 g ~t~~ ~~~.s~~~~°~w~w~~°~~~
With Today's Web environment, the semantics of information presented are lost upon conversation of the structured data to HTM:L at the server, meaning that the "knowledge" is stripped from the objects before the user has an opportunity to interact with them. In addition, Today's Web is authored and "hard-coded" on the server based on how the author "believes" the infornlation will be navigated and consumed. Users consume only information as it is presented to them.
The present invention adds a layer of intelligence and layers of customization that Today's HTML-based Web environment cannot support. The present invention provides an XML-based dynamic Web of smart knowledge objects rather than dumb Web pages wherein the semantics of the objects are preserved between the server and the client, thereby giving users much more power and control over their knowledge experience. In addition, with the Web of the present I11Ve11ti0I1, knowledge-workers are able to consume and act on information on their own terms because they will interactively author their own knowledge experiences via "dyllal111C linking" and "user-controlled browsing."
The Information Agent (semantic browser) of the present invention is designed to co-exist with Today's Web and to integrate with and augment all facets of private and public intranets as well as the Internet. The technology platform stacks of Today's Web and the Information Nervous System of the present invention are summarized in FIGURE
6. With reference to FIGURE 6, the stack for the Today's Web has at the bottommost layers Structured Information Sources, including such information as the data stored in databases, and Unstructured Information Sources, including such information as documents, email messages, etc. Information in both of these layers is handled distinctly. No semantics are used at the Information Indexing Layer; rai:her, search engines based on keywords 'are used.
The Logic Layer consists primarily of a database that allows programmability for searching, rules, view, triggers, etc. The Application Layer consists of server-side scripts that drive e-Easiness applications based on user input. At the topmost or Presentation Layer, Today's Web has presentation information (in the form of Web pages) that is exposed via portals with a Web platform (e.g., browser).
Apart from overlapping layers of processing, the present invention uniquely handles infornlation from the bottommost level of operation in a manner that preserves the semantics of the underlying information sources. At both the Structured and Unstructured Information Sources Layers, the system 10 handles information uniformly, taking into account metadata and semantics associated with the information. At the Information Indexing Layer, information metadata and semantics are extracted from unstructured. The system 10 adds three additional platform layers not present in Today's Web: Knowledge Indexing and Classification Layer, wherein information from both structured and unstructured sources are semantically. encoded; .Knowledge Representation Layer, wherein associations are created that allows maintenance of a self correcting or healing Semantic Network of knowledge objects; and Know ledge Ontology and l~nference Layer, wherein new connections and properties are inferred in the Semantic Network. At the Logic Layer a knowledge-base is created that allows for programmability .at a semantic level. At the Application Layer, server-side scripts are used in association with the knowledge-base. These scripts dynamically generate knowledge objects based on user input, and may include ,semantic commands for retrieval, notifications and logic. This Layer may also include Smart Agents to optimize the handling of semantic user input. The Presentation Layer of the system 10 preserves the semantics that are tracked from the bottommost layers.
Presentation at this Layer is dynamically generated on the client computer system and completely customizable.
>3y the maintenance, integration and use of semantics in all teelmology layers, the present invention creates a virtual Web of actionable "objects" that directly correspond to "things" that humans interact with physically or virtually or, in other words, as familiar "Context Templates." As opposed to Today's Web, which is a dumb Web of documents, the present in ~,ention provides for a smart virtual Web of actionable objects that have properties and relationships, and in which events can dynamically cause changes in other parts of the virtual Web.
The present invention provides a programmable Web. Unlike Today's Web which is a dumb Web of documents, the Web of the present invention is programmable akin to a database- it is able to process logic and rules, and will be able to initiate events.
While Today's Web is encoded fir human, and thus is focused primarily on presentation of static information, the virtual Web of the present invention is encoded primarily for machines, albeit ultimately presented to humans as the end of the knowledge delivery chain. The present invention provides an intelligent, learning Web.
This means that the virtual Web of the present invention will be able to learn new connections and become smarter over time. The Web is dynamic, virtual and self authoring, thereby providing much more power to knowledge-workers by intelligently and proactively making semantic connections that Today's Web is unable to provide, thereby leading to a reduction in and w eventual elimination of information loss.
The Web of the present invention is a self healing Web. Unlike Today's web which has to be manually maintained by document authors, the present invention,provides a- Web that is self maintained by machines. This feature rectifies broken links because the Web will fix disconnections in the netv~ork automatically.
Finally, as will be set forth in greai;er detail below, the various embodiments of the present invention incorporate some or all of the axes of knowledge acquisition described above to provide substantial advantages over existing systems directed to Today's Web or tlm conceptual Semantic Web.
C. SYSTEI11 ARCHITECTURE AND TECHNOLOGY CONSIDERATIONS
I. SYSTEM O 6BR VIEI1' The present invention is directed to a system and method for leno~~rledge retrie~ral, management and delivery. This system and method is referred to herein by the trademarked term Information Nervous SystemTM. With reference to FIGURE 7, at its highest level the system 10 includes a server 20 comprised of several components that work together to provide context and time-sensitive semantic information retrieval services to clients 30 operating a presentation platform (e.g., a bro~wser) via a communication medium 40, such as the Internet or an intranet. The server components preferably include a Knowledge Integration Server (KIS) 50 and a Knowledge Base Server (KBS) 80, which may be physically integrated or separate. Within the system, all objects or events in a given hierarchy are active Agents 90 semantically related to each other and representing queries (comprised of underlying action code) that return data objects for presentation to the client according to a predetermined and customizable theme or "Skin." This system contemplates.~v~ide variety of applications, as well as various means for the client to customize and "blend"
Agents and the 22s underlying related queries to optimize the presentation of the resulting inforniation. Each of the preferred components of the system 10 of the present invention, as well as the interaction among the components, is described in greater detail below.
2. SI'STEMARCHITECTURE
The end-to-end system architecture for the Information Nervous System of the present invention is shown with reference to FIGURE 8. FIGURE 8 illustrates how the present invention provides multiple client access means of communication, between the Information Nervous System XML Web Service (KIS) and Smart Agents. In the preferred embodiment, this occurs via the Information Agent. In an alternative embodiment, the communication may occur programmatically via an Enterprise Knowledge Portal (e.g., Today's Web access browser) or via an SI7K layer that enables programmatic integration with a custom client.
The system architecture for the KI S of the Information Nervous System, including components thereof, are shown with reference to FIGURE 9a These components are described ,in greater detail below.
TECIIN()L~t~ 3'.STACIfS
The significant differences between Today's Web and the conceptual Semantic Web are further highlighted by reference to the technology stacks of each as shown with reference to FIGURE 10. FIGURE 10 is a side-by-side comparison of the high-level descriptive platform layers of Today's Web and the equivalents (where applicable) in the Information Nervous System of the present invention. FIGURE 10 illustrates how scenarios in Today's Web map to scenarios in the Information Nervous System in certain instances, thus providing users with a logical migration path, but also highlights aspects of the Information Nervous System that do not exist in Today's Web.

4. SYSTEMHETEROGENEITY
Heterogeneity is an advantage of tine present invention. In the preferred embodiment, the KIS Agency XML Web Service is portable. This means that it supports open standards such as XML, XML Web Services that are interoperable (e.g., that employ the WS-I standard for interoperability), standards for data storage and access (e.g., SQL and ODBC/JDBC) and standard protocols for the information repositories from which the DSAs gather data (e.g., LDAP, SMTP, HTTP, etc.), etc.
For.example, i.n a preferl-ed embodiment, a KIS (on which an Agency is running) is able to:
~ Gather its "people" metadata from an LDAP store (using an LDAP DSA). This allows it to support Microsoft's Windows 2000 Active Directory, Sul's Directory Server, and other Directory products that support LDAP. This is preferable to havlllg a platform-specific Active Directory DSA that uses platform-specific APIs to gather "people" metadata.
~ Gather its email metadata from an SMTP store (for email from any source or for the system inbox). This allows it to support Microsoft Exchange Lotus Motes, and other email servers (which support SMTP). This is preferable to having a platform-specific Microsoft Exchange Email DSA or a Lotus Notes Email DSA.
~ Gather its "event" metadata from a calendar store supporting ~an open standard like iCalendar and use a protocol such as Calendar Access Protocol (CAP). This allows it to support any event repository that supports the iCalendar or CAL
protocol standard. This is preferable to having a platform-specific Microsoft Exchange Calendar (or Event) DSA, a Lotus Notes Calendar DSA, etc.
In an alternative embodiment, the KIS Agency may be configured to extract metadata stoxed in a proprietary repository (via an appropriate DSA).
To achieve heterogeneity, in the preferred embodiment, for client-server communications, the system 10 uses XIV1L Web Service standards that work in an interoperable manner (across platforms). These include appropriate open and interoperable standards for SOAP, XML, Web Services Security (WS-Security), Web Services Caching (WS-Caching), etc.

In the preferred embodiment of the present invention, the semantic browser (also referred to by the trademarked term Information AgentT"s) is able to operate Cross-platform and in different environments, such as Windows, .NET, JZEE, Unix, etc. This ability is consistent with the notion of a semantic user experience in that users do not arid should not care about what "platform" the browser is running on or what platform the Agency (server) is running on. The semantic browser of the present invention provides users with a consistent experience regardless whether they are "ta.lking" to a Windows (or .NET) server or a J2EE
server. Users are not required to take any extra steps while installing or using the client based on the platform on which any of the Agencies they are interacting with is running.
The Iliformation Agent preferably uses open standards for its Skins and other presentation effects. These include standards such as XSLT, SVG, and proprietary presentation formats that work across platforms (e.g., appropriate versions of Flash MX/ActionScript).
A sample, heterogeneous, end-to-end implementation of a preferred embodiment of the Information Nervous System of the present invention is shown with reference to FIGURE 1 f. FIGURE 11 illustrates the preferred embodiment of the Information Nervous System and illustrates the heterogeneous, cross-platform context for the present invention.
The components shown in FIGURE 11 are described in greater detail below.
S. SECURITY
The preferred embodiment of the Information Nervous System provides support for all aspects of security: authentication, authorization, auditing, data privacy, data integrity, availability, and non-repudiation. This is accomplished by employing standards such as WS-Security, which provides a, platform for security with XML Web Service applications.
Security is preferably handled at the protocol layer via security standards in the XML Web Se~ice protocol stack. This includes encrypting method calls from clients (semantic browsers) to servers (Agencies), support for digital 'signatures, authenticating the calling user 22s before granting access to an Agency's Semantic Network and XML Web Service methods, etc.
The preferred embodiment that the present invention supports local (client-side) credential management: This is preferably implemented by requiring users to enter a list of their usernames and passwords that they use on multiple Agenci-es (within an Intranet) or over the Internet. The semantic browser aggregates inforniation from multiple Agencies that may have different authentication credentials for the user: Supported authentication credentials optionally include , common schemes such as basic authentication using a username and password, basic authentication over SSL, Microsoft's .NET
Passport authentication service, the new Liberty Alliance authentication service, client certificates over SSL, digest authentication, and integrated Windows authentication (for use in Windows environments).
In the preferred embodiment, with the users'' credentials cached at the client, the semantic browser uses the appropriate credentials for a given Agency by checking the supported authentication level and scheme for the Agency (which i~ part of the Agency's schema). For example, if an Agency supports integrated Windows authentication, the semantic browser invokes the XML Web Service method with the logon handle or other identifier for the current user. If the Agency supports only basic authentication over SSL, the semantic browser passes either the usernarrne and password or a cached copy of the logon handle (if the client was previously logged on and the logon handle has not expired) in order to logon. The preferred embodiment employs techniques such as logon handle caching, aging and expiration on the KIS in order to speed up the authentication process (and logon handle lookups) and in order to provide more security by guarding against hijacked logon handles.
The Agency XML Web Service preferably supports different authentication schemes either .implicitly (if the feature is natively supported by the server operating system or application. server) or at the application-level by the XML Web Service implementation itself. Alternative embodiments of the KIS Agency's XML Web Service preferably employ a variety of authentication schemes such as basic authentication, basic over SSL, digest, integrated Windows authentication, and client certificates over SSL, and integrated .NET
passport authentication.
S G. EFFICIENCYCONSIDERfITlOiVS
Client-Side and Server-Side Query and Object Caches. The present invention provides for query caches, which are responsible for caching queries for quick access. On the client, the client-side query cache caches the results of SQML queries with specified arguments. The cache is preferably configured to purge its contents after a predetermined amount of time (e.g., a few minutes). The amount of time is preferably set by modeling system usage and arriving at an opfimal value for the cache time limit. Other parameters may also be considered, such as the data arrival rate on the Agency (in the case of per-Agency caches, which is another implementation option), the usage model (e.g., navigation rate) of the user, etc.
Caching improves performance beczuse the client does not have to needlessly access recently used servers as the user navigates the semantic environment. In the preferred a:~
embodiment, the client employs standard XML Web Service Caching technologies (e.g., WS-Caching). Ill~addition, on the client, there is preferably an object cache. This cache caches the results of each SQML resource and is tagged with the resource reference (e.g., the file path, the URL, etc.). This optimizes SQML processing because the client can get the XML metadata for an SQML resource directly From the object cache, without having to access the resource itself. The resource may be the local file system, a local application (e.g., Microsoft Outlook), or an Agency's XML Web Service. Like the query cache, the .
object cache maybe configured to~ purge its contents after a set amount of time (e.g., a few minutes).

In an alternative embodiment, on the server, the .server-side query cache caches the category results for XML arguments. This speeds up the query response time because the server does not have to ask the KDM to categorize XML arguments (via the one or more instances of the KBS that the KIS is configured to get its domain knowledge from) on each query request. In addition, the server can cache the SQL equivalents of the SQML arguments it receives from clients. This speeds up the query response time..because the server would not have to convert SQML arguments to SQL each time it receives a request from a client. In the preferred embodiment, aggressive client-side caching is, employed and server-side caching is avoided unless it clearly improves performance. This is because client-side caching scales better than server-side caching since the client caches requests based on its local context.
virtual, I~ista-ibuted Queries. Tlne present invention employs virtual, distributed queries. This is consistent with its "dynamic linking" and "user-controlled browsing"
functionality. The system does not require static networks that link-or massive individual databases that house-all the metadata for the system. This precludes the need for manual auth(7Tlllg and maintenance on a local or global scope. In addition, this precludes the need for integrated (or universal) storage, wherein all the metadata is required to be stored on a~single metadata store and accessible through one database query interface (e.g., SQL). Rather, the present invention employs the principle of "Dynamic Access" via its use of XML
Web Services to dynamically distribute queries across 'various Agencies (in a context and time-sensitive manner), and to aggregate the results of those queries in a consistent and user-friendly manner on the client.
D. SYSTEM COMPONENTS AND OPERATION
1. AGENCIES AND AGENTS
The present invention introduces a unique approach to using Agencies and Agents to retrieve, manage and deliver knowledge.

a. Agencies In a preferred embodiment of the present invention, the Agency is an instance of the IW owledge Integration Server (KIS) 50 and is the invention's equivalent of a Web site. An Agency is preferably installed as a Web application (on a Web server) so as to expose XML
Web Services. An Agency will preferably include an Agency administrator. In a preferred embodiment of the present invention, an Agency has the following primary components:
~ A flag indicating whether the Agency supports or requires authentication (or both). If the Agency requires authentication, the Agency will require basic user information and a password and will store information on the type of authentication it supports. For Agencies that store user information, the Agency will also require user subscription infornzation (for subscription to Agents on a specific Agency).
Stnictured stores of semantic: objects (documents, email messages, etc.)-Corresponding to schemas for tine respective classes.
~ Runtime components that respond to semantic queries-Components return XML
to the calling application and provide system services for all the information retrieval features of the semantics browses.
Server-Side User State. In the prefen-ed embodiment of the present invention, Agencies support server-side User State, which associates related concepts including "people" metadata and user authentication. Server-side User State facilitates many of the implementation details of the present invention, including the storage of user favorites (by semantic links between people objects and information objects), the inference of favorites in order to generate new links (e.g., recommendations), Annotations (that map users' comments to information objects), and the inference of "experts" based on semantic links that map users to information (e.g., posted emails, annotations, etc.). Server-side User State is preferably used with some Context Templates like "Experts," "Favorites,"
Recommendations," and "Newsmakers." ' Client-Side User State. The Information Agent (semantic browses) preferably supports roaming of local client-side User State. This includes users' Semantic Environment and users' credentials (securely transferred). In the preferred embodiment, users are able to easily export their client-side User State to another machine in order to replicate their Semantic Environment onto another machine. This is preferably achieved by transferring users' Agent list (recent and favorites), the metadata for the Agents (including the SQML
buffers), users' local security credentials, etc. to an XML format that serializes all this state aiid enables the state to be easily transferred. Alternatively, an XML schema may be developed for all the local client-side User State. Caching the User State on a server and synchronizing the User State using common synchronizationtechniques can also facilitate roaming. The semantic browser preferably downloads and uploads all client-side User State onto the server, rather than storing the state locally (in an XML file or a proprietary store like the Windows registry).
b. Agents An Agent is the main entry point into the Semantic Network of the present invention.
An Agent preferably consists of a semantic filter query that returns XML
information for a particular semantic object type (e.g., documents, email, people, etc.). In other words, an Agent is preferably configured with a specific object type (described below).
Agents can also be configured with a Context Template (described below). In this case, the query will return an object type, but it will incorporate the semantics of the Context Template.
For example, Agents configured with a "Headlines" Context Template will be sorted by time and ,20 relevance, etc. Agents are also used to filter notifications, alerts and announcements. Agents can be given any name. However, in the prefer-ed embodiment of the present invention, the naming format for most Agents is:
<Agentobjecttype>.<semanticqualifier>.<semanticqualifier>
Agents can be named arbitrarily. However, examples of Agent names include:
AILAII
Email.All Documents.Teclmology. Wireless.80211 B.AII
Events.Upcoming.NextThirtyDays.All There will also be Domain Agents (see below) that may follow a different naming convention (see below). At the semantic browser of the present invention, a fully qualified Domain Agent name will have the format:
<Agentobj ecttype>.<semanticdomainname>.<categoryname>
[Agency=<Agency url>, kb=<kb url>]
For example, the Email Domain .Agent on the Agency http://research.Agency.asp configured with the category wireless.all from the lmowledge-base ABC.comlkb.asp with the semantic domain name industries.informationtechnology will be fully named as:
Email.Industries.InfonnationTechnology.Wireless.All [Agency=http://researchlAgency.asp, kb="http:l/abccorp.comlkb.asp"
The semantic browser of the present invention is preferably configurable to use only the Agent name or to include the "Agency" and "kb" qualifiers.
Agent Types. There are three primary types ~f Agents created on server 20:
Standard Agents, Compound Agents, and Domain ~sgents. A Standa~°d Agent is a standalone Agent that encapsulates structured, non-semantic queries, i.e., without domain knowledge (or an ontology/taxonomy mapping). For example., on the server, the Agent All.PostedToday.All is a simple Agent that is resolved by filtering all objects based on the CreationTime property.
Standard Agents can also be more complex. For example, the Agent All.PostedByAnyMemberOfMyTeam.All may resolve into a complicated query that involves joins and sub-queries from the Objects table and the Users table (see below).
A Compound Agent contains other Agents and allows the Agency administrator to create queries that generate results that are the UNION or the INTERSECTION of the results of their contained Agents (depending on the configuration). Compound Agents can also contain other Compound Agents. In the presently preferred embodiment, Compound Agents contain Agents from the same Agency. Ffowever, the present invention anticipates the integration of Agents from different Agencies. By way of example, a Compound Agent All.Technology.Wireless.All might be created by compounding the following Agents:
~ Documents.Technology.Wirele~;s.All ~ Email.Teclunology.Wireless.Al1 ~ People.Experts.Technology.Wireless.All . As described above, a Dorrrain Agent is an Agent that belongs to a semantic doIllalll.
A Domain Agent is initialized with an Agent query, just like any other Agent.
However, this query includes the CATEGORIES table, which is populated by the K.lowledge Domain Manager (see below). While the preferred embodiment of the present invention utilizes a KBS 80 having proprietary ontologies corresponding to a private Semantic Envirolm~ent, the present invention contemplates integrated support of ontology interchange standards that will enable an Agency to connect to one or more custom private TABS, for example within an organization where the Agency was previously initialized with a proprietary ontology for that organization.
An e~arrlple of a De~main Agent i 9 Email.Technolog~r.'~6pireless.t~ll. This agent is preferably created with a knowledge source URL such as:
category://technology.wireless.all a ABC.com/marketingknowledge.asp This knowledge source URL corre:>ponds to the Technology.Wireless.All category for the default domain on the knowledge base installed on the ABC.com/marketingknowledge.asp Web service. This is resolved to the following HTTP
URL:
http://ABC.com/marlcetingknowledge.asp?category=''techrlology.wireless.all."
In this example, a fully qualified version of the category URL may be:
category:l/technology.wireless.all@abccorp.comimarketingknowledge.asp?se manticdomainname="InformationTechnology"
In this case, the category URL is qualified with the domain names.
°Domain Agents are preferably created via a Domain Agent Wizard, and the Agency administrator is able to add Domain Agents i:rom the KBS 80 to the Semantic Network of the present invention. The Domain Agent Wizard allows users to create Domain Agents for specific categories (using a category URL) or for an entire semantic domain name. In the latter case, the Agency is preferably configured to automatically create Domain Agents as new categories are added to the semantic domain on the KBS. This feature allows domains and categories to remain dynamic and therefore easily adaptable to the user's needs over time. When Domain,Agents are managed i:n this fashion, the Agency is configurable so as to remove Agents that are no longer in the semantic domain. Essentially, in this mode, the Domain Agents are synchronized with the CATEGORIES table (which in turn is synchronized with the CATEGORIES list at the relevant KBS by the Knowledge Domain Manager, described below).
A Domain l~gent is initialized vrith ~ ~sti-uctured query that filters the data the Agent manages based on a category name or URL. In this situation, the structured query is identical to the queries for Standard Agents. An example of a resultant query for a category Agent is:
SELECT OBJECT FROM OBJECTS WHERE OBJECTID 1N (SELECT
1 ~ OBJECTID FROM SElYIAT~TTICLI7VI~S WHERE PREDICATET~PEID=50 AND SUBJECT=1000 AI'1D O>3fECTID lint (SELECT O)3JECTID FROM
.CATEGORIES WHEF',E ~ URL LIKE
category:l/technology.wireless.all@ABC.com/kb.asp?domain="marketing")) '~
In this example, the "belongs to the category" predicate type ID is assumed to have the value~50, and the category objectid is assumed to have the value 1000. This query can be translated to English as follows:
Select all the objects in the Agency that belong to the category whose object has an objectid value of 1000 and whose URL is category://technology.wireless.all@abecorp.comlkb.asp?domain="marketing"
This in turn translates to:
Select all the objects in the Agency of the category category://technology.wireless.all@abccorp.com/kb.asp?domain--"marketing"

The Domain Agent Wizard asks the user whether he or she wants to name the Agent based on the short category name or a friE:ndly version of the fully qualified category name.
An example of the latter is: Marketing.Technology.Wireless.All [@ABC]. The fully qualified Domain Agent naming convention is:
<objecttypename>.<semanticdomainname>.<categoryname>.all [@KB Name].
., In this example, the Domain Agent, name i;>:
Eniail.Marketing.Technology.Wireless.All [@ABC].
Blenders. Blenders are users' personal super-Agents. Users are able to create a Blender and add and remove Agents (across Agencies) to and from the Blender.
This is analogous to users having their own "Personal Agency". Blenders are preferably invoked ~nly on the system client since they includf< Agents from multiple Agencies.
The client of the present invention aggregates all objects from a Blender's Agents and presents them appropriately. Blenders preferably include all manipulation characteristics of other types of Agents, e.g., drag and drop, Smart Lens (see below). A Blender can contain any type of Agent (e.g., Standard Agents, Search Agents, Special Agents, as well as other Blenders).
The present invention provides for a Blender Wizard, which is a user interface designed to facilitate users in creating Blenders. FIGURES 12-14 show exemplar screenshots of aspects of the Blender Wizard user interface according to a preferred embodiment of the present invention. FIGURE 12 is a lsample Information Agent screenshot showing a Tree View of a sample Semantic Environment and a sample of the "Add Blender" wizard that allows users to create and manage a new Blender. FIGURE 13 shows the second page of the Add Blender wizard where users enter the name and description of the Blender and optionally select information object type :filters. FIGURE 14 shows the third page of the sample Add Blender wizard in accordance with a preferred embodiment of the present . invention. In this example, users add and rf;move Agents from the Semantic Environment to or from the Blender. When the "Add Agents" option is selected, the "Open Agent" dialog is displayed from which users can add a new Agent, Blender or Agency to the new Blender.
Breaking News Agents. A Breaking News Agent is a specially tagged Smart Agent.
In addition to the option of having time-criticality being defined by the Agency administrator, the user has the option of indicating which Agents refer to infonmation that he or she wants to be alerted about. Any information being displayed will show alerts if there is breaking news that relates to it on a Breaking News Agent. For example, a user will be able to create an Agent as: "All Documents Posted on Reuters today" or "All Events relating to computer technology and holding in Seattle, in the next 24 hours"' as Breaking News Agents.
This feature functions in an individual way because each Breaking News Agent is personal "breaking" is subjective and depends on the user). For example, a user in Seattle perhaps would want to be notified on events in Seattle in the next 24 hours, events on the West Coast in the next week (during which time he or she can find a cheap flight), events in the United States in the next 14 days (the advance notice for most U.S. air carriers to get a modestly priced cross-continental flight), events in Europe in the next month (likely because he or she needs that amount of time to get a hotel reservation), and events anywhere in the world in the next six months.
In a preferred embodiment, the present invention automatically checks the Semantic Enviromnent for breaking news by queryin~; each Breaking News Agent or by querying the "Breaking News" Context Template. It will do this for all objects displayed in the semantic browser window. If a Breaking News Agent indicates that there is breaking news, the Information Agent object Skin so indicates by flashing the window or by showing a user interface that clearly indicates that there is an alert that relates to the object. When the user clicks on the breaking news icon, a breaking news pane or a Context Palette for the "Breaking News" Context Template is displayed allowing the user to see the breaking news, select the Breaking News Agent (if there are multiple with breaking news), select predicates, and select other options: An exemplar pane of a Breaking News Agent user interface is shown in FIGURE 15. This sample user interface illustrates the popup menu in the context Results Pane. The sample shows a similar context pane as a Smart Lens (Agent-Object) popup context Results Pane (discussed below) except that the Agent is a Breaking News Agent.
Default Agents. In an alternative embodiment, each Agency exposes a list of default Agenfs. Default Agents are similar to. the default page on a Web site; authors of the Agency determine which Agents they want users to always sees. Alternatively, on the client,wDefault Agents may be invoked when users click on the root of the Information Agent's Environment (which preferably corresponds to a "Home Agent," for example, the equivalent of the "Home Page" on Today's Web browser). Combined Default Agents niay also be configured by users.
Default Special (or Context) Agenta. In the preferred embodiment, the client or the Agency support a Default Special or Context Agent that maps to each Context Template (discussed below). These Agents preferably use the appropriate Context Template without any filter. For example, a Default Special Agent called "Today" returns all items on all Agencies in the "recent" and "favorites" lists (or on a configured list of Agencies) that were posted today. In yet another example, the Default Special Agent called "Variety" shows random sets of results for every Agency in 'the Semantic Environment corresponding to the "variety" Context Template.
Default Special Agents preferably function as. a starting point for most users to familiarize themselves with the Information Nervous System of the present invention. In addition, Default Special Agents retain the same functionality as Smart Agents, such as use of drag and drop, copy and past, Smart Lens, Deep Information, etc.
Horizontal Decision Agents. W the preferred embodiment, Agents utilized by the client to assist with user interaction, including:

~ Schedule Agent: The Schedule Agent intelligently ranks events based on the probability that particular users would want to attend the event.
~ Meeting Follow-up Agent: Tlue Meeting Follow-up Agent intelligently notifies users when the time has come to have a follow-up meeting to one that occurred in the past. The Inference Engine (see below) monitors relevant semantic activity to determine whether enough change has occurred to warrant a follow-up meeting.
Users preferably use the previous meeting object as an Tnformation Object Pivot to find the relevant knowledge <;hanges (such as new documents, new people that might want to attend, etc.) ~ Task Follow-up Agent. The Task Follow-up Agent sends recommendations to users in response to tasks users perform (such as readirig a document, adding an event to their calendar, etc.). Tl.te Agent ensures that users have constant follow-up. The recommendations are based on users' profile, and the Agent preferably uses collaborative filtering to determine recommendations.
0 customer Follow-up Agent. The Customer Follow-up Agent sends notifications to users based on customer activity. The Agent intelligently determines when the user needs attention (based on email received from the user, new documents that might aid user service, etc.) Puhlie a~e~-~u~ L~cal Agent. Agent:9 that are created by the Agency administrator are "Public Agents." Agents created and managed by users are "Local Agents."
Local:. Agents can refer to remote Agencies via SQML that includes references to Agency XML
Web.
Service URLs, or can refer to local Agencies that run a local instance of the KIS with a local metadata store.
Saved Agents-Users' My Agents :List. In the preferred embodiment, users are able to save a copy of an invoked Agent or a query result as a local Agent. For example, users may drag and drop a document on their hard drive to an Agent folder to generate a semantic relational query. Users could save that result as an Agent named "Documents.Technology.Wireless.RelatedToMyDocument." This will then allow the user to navigate to that Agent to see a personalized semantic query. Users would then be able to use that Agent to create new personal Agents, and so on. Personal Agents can also be "published" to the Agency. Other users are preferably able to discover the Agent and to subscribe to it.
In the preferred embodiment, a local Agent is created by a "Save as Agent"
button that appears on the client anytime a semantic relational query result is displayed. This is analogous to users saving a new document. Once the Agent is saved, it is added to the users' My Agents list. An Agent responds to a semantic query based on the semantic domain of the Agency on which it is hosted. Essentially,, a semantic query to an' Agent is analogous to asking whether the Agent "understands the query." The Agent responds to a.guery to the best of its "understanding." As a further illustration, an Agent 'that manages "People" responds to a semantic query asking for experts for a document based on its own internal mapping of people in its semantic domain to the categories in that domain.
Alternatively, the system client may be configured ,to use non-semantic queries. In this case, the Agency will use extracted keywords for the query. All Agents support non-semantic queries. Preferably only Agents on 'Agencies that belong to a semantic domain rvill support semantic queries. In other words, semantic searches degrade to searches.
Each Agent has an attribute that indicates whether it is "smart" or not. A
Smart Agent is preferably created on an Agency if that Af;ency belongs to a semantic domain. In addition, a Smart Agent only returns objects it fully "u.nderstands." In the preferred embodiment, when an Agency is installed, there are several default Smart Agents that the Agency administrator may optionally choose to install, including:
~ All.Understood.Al1 ~ Documents.Understood.All ~ Email.Understood.All For example, Email.Understood.Al1 emly returns email objects that the Agency can semantically understand based on its semantic domain (or ontology).

The present invention preferably includes the capability for users to display all objects and only those the Agency understands Search Agents. A Search Agent is an Agent that is initialized with a search string. In the preferred embodiment, on invocation, the client issues the search request.
A Search Agent S is configurable so as to search any part of the Semantic Environment, including:
~ Frequently Used Agents ~ Recently Used Agents ~ Recently Created Agents ~ Favorite ~ All [Saved] Agents ~ Deleted Agents ~ Agents on the local area network o Agents on the Global Agency Directory ~ Agents on any user-customized Agency directories ~ All Agents in the entire Semantic Environment The client issues the search request based on the scope of the Search Agent.
If users indicate that they want the search to cover the entire Semantic Environmaent, the client issues the request to all Agents in the Semantic Environment Manager (see below) and all Agents on the local area network, the Global Agency Directory and user-customized Agency Directories.
Server-Side Favorite Agents. In yet an alternative embodiment, the Agency supports User States support Favorite Agents. In the analogous context of Today's Web, a Web site allows users to customize their favorite links, stocks, etc. When initially queried, an Agency displays both its Default Agents and the Favorite Agents of the calling user (if there is a User State).
Smart Agents. A Smart Agent is a standalone Agent that encapsulates structured, semantic queries that refer to an Agency via its XML Web Service. In the preferred embodiment, user on the client are able to create and edit Smart Agents via a "Create Smart Agent" wizard that allows them to browse the Semantic Environment via the Open Agent dialog, and add links from specified Agencies. Essentially, this corresponds to users creating the SQML query from the user interface. In. the preferred embodiment, the user interface only allows users to add links from the same Al;ency resource. However, users can create Agents . of the ame categories across Agencies, in. addition to Special Agents and Blenders (which are also preferably cross-Agency). The user interface allows the user to add links using existing Smart Agents as Inforniation Object Pivots provided that the Smart Agent refers to the same Agency for the current query. FIGURE 16 illustrates a preferred embodiment showing the Open Agent dialog with the user interface controls for selecting link (predicate) templates, the links themselves, and the objects. FIGURES 17-19 illustrate the Tree View of a sample Semantic Envirornnent involving- the Open Agent dialog. FIGURE 17 shows the Open Agent dialog allowing users to browse the Semantic Environment and open an Agent., FIGURE 18 illustrates a way of navigating Agencies in the Semantic Environment and the "Open Agent" dialog with the "Small Preview" view. FIGURE 19 illustrates an "Open" tool on the toolbar showing new options to open Agents form the Semantic Environment or to import regular information (e.g., from the file system) to the Semantic Environment by creating Dumb Agents.
The link templates essentially allow the user to navigate predicate for the current object type using predefined filters, thus allowing the user to avoid going through all the predicates for the object type. Examples of link templates include:
~ All ~ Breaking News (links that refer to time-sensitivity, e.g., "posted in the last") ~ Categorization ~ Definite (non-probabilistic links) ~ Probable (probabilistic links) Annotations In the preferred e117bOd11nellt, the Open Agent dialog allows the user to select the object to "link to" and, depending on the type of the object, allows the user to browse the object (e.g., from a calendar control if it is a dateltime, from a text box if it is text, from the file system if it is a file or folder path, etc.) The wizard user interface also allows the user to S preview the results of the query. A temporary SQML entry is created with the current predicate list and that is loaded in a mini-browser window within the wizard dialog box. The user is able to add and remove predicates, and will also have the option of indicating whether he or she wants a union (an "OR") or an intersection (an "AND") of the predicates. The user interface will also check for duplicate predicates.
Once the user finishes the wizard to create the Smart Agent, the Smart Agent is added to the Semantic Environment and the SQMI, is also saved pith the associated object entry. In tloe preferred embodiment, the user can, later browse the Smart Agent using the Agent property inspector property sheet. This allows the user to view the simple Semantic Environment properties (e.g., name, description, creation time, etc.) and also to view the resource URL (the WSDL LJRL to the EMI, Web Service of the Agency being queried) and the predicate list. The user can edit the list from the property sheet.
Default Smart Agent. A Default Snnart Agent is similar.to a Default Special Agent except that it is based on information object types and not on Context Templates. By way of example, "Docul-lients" would return all documents on all Agencies in the users' Semantic Environment; "Email" would return all email messages in user's Semantic Environment, etc.
Special Agent. A Special Agent is a Smart Agent created by users based on a Context Template (see below). A Special Agent is preferably initialized with an Agent name, albeit without a specific Agent reference. For example, a Special Agent "Email.Technology.Wireless.All" may be created even if there are no Agents of that name in the Semantic Enviromnent. Like a Search Agent, a Special Agent is scoped to search for any Agent with its name on any part of the Semantic Environment. In the preferred embodiment, when a Special Agent is invoked by users, the client searches for any Agents that bear its name. If or when it finds any Agents 'with the name, the client invoke the Agent.
In the preferred embodiment, users enter parameters consistent with a Context Template, indicating the category fillers (if required) and what Agency(ies) to query. These can be manually entered using the Open Agent dialog, or users can indicate that they want to query the "recent" Agencies, "favorite" Agencies, or both. In an alternative embodiment, users~have the choice of selecting categories (if required) that are in the union or intersection of the selected Agencies, or all categories known to the Global Agency Directory. In yet an alternative embodiment, users are able to select the information type (as opposed to a Context Template) and keywords to search (as opposed to predicates or categories).
I~-efault Special Agents. In the preferred embodiment, the system client installs Default Special Agents that map to all supported Context Templates. By way of example, in the preferred embodiment, Default Special .gents including the following:
Headlines Breaking News Conversations Newsmakers Upcoming Events Discovery History All Bets Best Bets Experts Favorites Classics Recommendations Today Variety Timeline Upcoming Events Guide Custom Special Agents. In contrast to user-created Special Agents, Custom Special Agents are Special Agents specially developed and signed in order to guarantee that the Special Agents 'are safe, secure, and of higlx-performance. The present invention provides for . a plug-in layer to allow organizations and developers to create their own custom blenders. An example of a custom blender is "All.CriticalPriority.Al1 that relates to my most recent documents or email.." This Custom Blender may be implemented by an SQML file with a resource entry as follows:
<resource type= "nervana:url"
agent://all.criticalpriority.all@localhost>
clink predicate= "nervana:relevantto"
type= "ueivana:localsenxanticref' recentdocuments >
</link>
~ clink operator= "or"
type= "nervana:localsemanticref"
recentemail>
</link>
</resource>
In the preferred embodiment, the Presenter (see below) resolves . lie "link"
entry locally and initiates XML Web Service requests to the target resource with XML
arguments corresponding to the newest documents or c°mail messages. This allows the target Agent to focus on responding to semantic queries purely with XML filters without knowing the semantics related tv ,filter origination. In an alternative embodiment, a Custom Blender such as the above example is a Default Agent.
Vertical Decision Agents. Vertical Decision Agents are Agents that provide decision-support for vertical industry scenarios.
Agent Schema. Agents operate within specified parameters and exhibit predetermined characteristics that comprise the Agent schema. Agent schemas may vary widely with being equally applicable within the technology of the present invention. By way of example only, the Agent schema of the preferred embodiment of the present invention is shown in FIGURE 20. The present invention specifically contemplates the addition of further fields. For example, fields for category URL (or path) and Context Template name can be added to the Agent schema to provide the client and server quick access to the category and Context Template the Agent represents (if applicable). This is helpful for the Semantic Environment Manager to provide different views ~of Agents (by category, by context, etc.).
This complements the existence of these fields in the SQM.L..for the Agent (expressed via attributes and/or predicates). The AgentT~rpeIDs included in the preferred' embodiment are shown in FIGURE 21. The AgentQueryT;ypeIDs included in the preferred embodiment are shown in FIGURE 2,2.
In the preferred embodiment, SQL query formats are used. However, multiple query forn~ats, for example XQL, XQuery, etc., are contemplated within the scope of the present invention.
The ISIS SO preferably hosts an Agents table (for server-side Agents) in its data store corresponding to this schema. FIGURE 23 illustrates sample semantic queries that correspond to Agent names showing how s~°rver-side Agents are preferably configured on the KIS of the present invention.
As explained in greater detail below, Agents may optionally include their own Skins.
An Agent Skin is represented as an URL to an XSLT file or equivalent Flash MX
or ActionScript. If the A,gent's Skin URL is not specified, a default Skin for the Agent's object type is presumed.
Agent Query Rules. Each server-side Agent query must be specified to return the OBJECTID column. Each table has this column for it is what links the Objects table with the tables for the derived object type. Objects and other tables are described in greater detail below.

Because each Agent query can form the basis of a sub-query, cascaded query or a join, it is preferable that each query follow this format. By way of example, the query for News.All will be may appear as "SELECT OBJECT1D FROM NEWS" (where "NEWS" is the name of the table hosting metadata for news articles, with the "news"
schema). As a result, the server 10 can then use this query as part of a complex query. For example, if the user drags and drops a document onto the cogent, the server might execute this query as:
SELECT OBJECTID FROM NEWS WHERE OBJECTID IN (SELECT
OBJECTID FROM SEMANTICLI'.NKS WHERE SUBJECTID IN (50, 67, 89) AND LINKSCORE > 90) This example assumes that the document is classified to belong to categories in the CATEGORIES table with object identifiers 50, 67, and 89 and that a link probability of 0.9 is the threshold to establish that a document belongs to a category. In this example, the document is used as a filter for the News.Al1 query and the query text is used as part of the complex query.
Ha~rin~ a consistent standard for queries allows the semantic query processor to merge queries until they finally have to be presented. For example, each call to the semantic query processor must indicate what object: type in which to return the results. The query processor then returns XML information consistent~with the schema for the requeste4d object type. In other words, the query processor preferably returns, schema-specific results for presentation. Each query is stored at the semantic layer (to return an OBJECTll~). To use the last example, when the user invokes the News.All Agent, the browser calls the query processor on the Agency XML Web Service. The query processor will then invoke the query and filter it with the 'News Article' object type, as such:
SELECT * FROM NEWS WHERE OBJECTID IN (SELECT OBJECTID
FROM NEWS) This returns all the fields for the News schema. The browser (via the Presenter) displays the information using the XSLT (or a presentation tool such as Flash MX or ActionScript) for either the Agent Skin or for a user-specified. Skin (which will override the Agent Skin).
Query Virtual Parameters. Agf.nt queries preferably contain special Virtual Parameter. A typical example may include: '%USERNAME%. TIa this example, the Semantic Query Processor (SQP) resolves the Virtual Parameter to a real argument before invoking the query.,An Agent People.MyTeam.All is configured with the SQL query:
SELECT * FROM USERS WHERE Division IN (SELECT Division FROM
USERS WHERE Name LIKE %USERNAME%) In this example, the Agent name includes "MyTeam" even though the Agent can apply to any user. The °IoUSERNAME% variable is resolved to the actual calling user's name by the SQP. The SQL call is resolved to as follows:
SELECT * FROM USERS WHERE Division IN (SELECT Division FROM
USERS WHERE Name LIKE JohnDoe) In this examplep JcahnDoe is assunmd to be tlm user name of the caller.
Simple Agent Search. Each Agent will support simple search functionality. In the preferred embodiment, a user is able to right-click on a Smart Agent in the Information Agent and hit "Search." This will bring up a dialog box where the user enters search text.
This creates the appropriate SQML with the associated predicate, e.g., "nervana:contains".
The present invention provides a simple, fast way for users to search Agents (and create Smart Agents from there) without going through the "Create Smart Agent" wizard and selecting the "contains text" predicate (which alternatively achieves the same result).
Agency Agent Views. An alternative embodiment of the present invention includes Agency Agent Views. An Agency Agent View is a query that filters Agents based on predefined criteria. For example, the Agent view "Documents" returns only Agents that manage.,objects of the document semantic c:fass. The Agent view "Reuters News"
returns a list of Agents that manage news objects with "Reuters" as the publisher.
Agency Agent Views are impoutant in order to give users an easy way to navigate through Agents. The Agency administrator is able to create and delete Agent views.
Agent Publishing and Sharing. The preferred embodiment makes it easy for Agents S to be published and shared. This is prefi°rably implemented by serializing the Semantic Environment into an XML document containing the recent and Favorite Agents, their schema, their SQML buffers, etc. and publishing the document to a publishing point. This XML document niay also be emailed to colleagues, friends, etc. in order to facilitate the propagation and sharing of local (user-created) Agents. This is analogous to how Web pages are published today and how web URL.s and' links are shared by sending links and attachments via enlail.
Z. ICNOI VLEDGE INTEGRATION SER VER
The Knowledge Integration Server (KIS) 50 is the heart of the server-side of the systeixa 10. The KIS semantically integrates data from multiple diverse sources into a Semantic Network and hosts Agents that provide access to the network. The KIS
also hosts semantic XML Web Services to provide clients with access to the Semantic Network via Agents. To users, a KIS installation may be viewed as an Agency. The KIS is preferably initialized with the following properties:
~ Agency Name. Nanie of the Agency (e.g., "ABC") ~ Agency Friendly Name. Full name of the Agency (e.g., "ABC Corporation") ~ Agency Description. Description of the Agency ~ Agency , System User Name. User name of the Agency. Each Agency is represented by a user on the directory of the enterprise (or Web site) on which it is installed. The system user name is used to host the system inbox (through which users will publish documents, email and annotations to the Agency). For authentication, the Agency must be installed on a server that has access to the system user account.
2so ~ Agency Authentication Support Level. Indicates whether the Agency supports or requires user authentication. An Agency can be configured to not support authentication (in which case it is open to all users and does not have any User State), to support but not require authentication, and to require authentication, in which case it preferably indicates the authentication encryption type.
~ Agency User Directory Type., This indicates the type of user. directory the Agency authenticates users against and where the Agency gets its user information frog. For example, this could be an LDAP directory, a Microsoft Exchange 2000 User Directory, or a Lotus Notes User Directory on the Windows 2000 Active Directory, etc.
~. ~ Agency User Directory Name. This indicates the server name of, the Agency user directory (e.g., a Microsoft Exchange 2000 server name).
~ Agency User Domain Name. 'Chis indicates the name of the user domain for authentication purposes. This field is optional and included only if the -Agency supports authentication.
~ Agency User Group Name. This indicates the name of the user group for authentication purposes. For example, an Agency might be initialized with the domain name "US Employees" and the group name "Marketing." In such a case, the Agency will first check the e.~ser nanm to ensure that the user is a member of the user group, and then forward authentication requests to the user directory authenticator indicated by the user directory type. If the calling user is not a member of the user group, the authentication request is denied. This field is only valid if the Agency supports authentication.
~ Data Store Connection Name. This indicates the name of the connection to a database store. This could be represented as, say, an ODBC connection name on Windows (or a JDBC name, etc.). The KIS will use the database referred to by the connection name to store, update, and maintain its tables (see below).
Dynamic Properties Evaluation. The Agency XML Web Service preferably exposes methods to return dynamic properties such as the list of semantic domain paths the server currently supporia or "understands." This allows users to browse Agencies on the client using their supported semantic domain paths or ontologiesltaxonomies.
As illustrated with reference to FIGURE 24, the KIS 50 preferably includes the following main components: a Semantic Network 5:?, a Semantic Data Gatherer 54, a Semantic 2s1 Network Consistency Checker 56, an Inference Engine 58, a Semantic Query Processor 60, a Natural Language Parser 62, an Email ~~nowledge Agent 64 and a Knowledge Domain Manager 66.
a. Semantic Nehvork The Semantic Network is the core data component of the KIS. The Semantic Network links objects of the defined schemas of thE: present invention together in a semantic way via database tables. The Semantic Network consists of schemas and the Semantic Metadata Store (SMS). The Semantic Network is preferably comprised of two data schemas:
Objects and SemanticLinks. Additional data schemas m.ay be included based on system requirements and enterprise needs. The SMS is preferably a standard database (SQL Server, Oracle, DB2, etc.) where all S~IllalltlG data is stored and updated via database tables. The SMS
preferably includes tables for each primary object type (described below).
By way of example, a sample Semantic Network directed towards an enterprise situation is shown with reference to FIGUEZE 25? which illustrates the relationship bet~~reen business users of the present invention and. the various sources of and results of knowledge retrieval, management, delivery and presentation.
~bjects. The Objects table contaivs every object in the Semantic Netwark. The "Object" can be thought of as the "base class" from which everysemantic object type will be derived. The preferred schema of the Object type is shown with reference~to FIGURE 26.
The ObjectID is a unique identifier that tags the object in the Semantic Network. Every object in the system will have a schema that is an extension of the Object schema.
Alternatively, semantic object types (e.g., document, email, event, etc.) will have only the ObjectID field. When a query is invoked, the query processor can then aggregate information from the Object table and the specific semantic table to form the final results. The former approach (having each schema be an extension of the Object schema) results in better runtime performance since joins are avoided. However, the latter approach, while computationally more expensive, results in less wasted storage. The ObjectTypeID is preferably a number that resolves to a string that describes the hierarchy of the object type, e.g., "documents\documents"; "documents\analyst briefs"; and "events~rneetings."
The SourceID refers , to the identifier for the Semantic Data Adapter (SDA) from which the object was gathered. The Semantic Data Gatherer (SDG) uses this information to periodically check whether the object still exists by requesting status inforn~ation from the SDA from which the object was retrieved.
SemanticLinks. The SMS preferably includes a SemanticLinks schema (and corresponding database table) that will store semantic links. These links will annotate the objects in the other data tables of the SMS and will preferably constitute the data model for the Semantic l~Tetwork. Each semantic link will have a semantic link ID. The SemanticLinks table preferably includes the field names and types as shown with reference to FIGURE 27.
The SubjectID and SubjectTypeID are the object ID and object type ID of the object being linked from. The ObjectID and ObjectTypeID are the object ID and object type ID of the object being linked to. The Lin7cScore preferably ranges from 0 to 100, and represents the semantic strength of the link as a probability. These fields are exemplary only; more predicates are contemplated based on the particular object type as well as the user's desire to semantic links. The preferred embodiment: of the present invention provides the predicate type IDs shown in FIGURE 28. The present invention contemplates the addition of further predicate type IDs.
By way of example, the semantic link "Steve reports to Patrick" will be represented in the table with a subject ID corresponding to Steve's ID in the Users table, a predicate type of PREDICATETYPEID REPORTSTO (see table below), Patrick's object ID in the Users table, a link score of 100 (indicating that it is a "truth" and that the link is not probabilistic) and a Reference Date that qualifies the link.

The KIS creates, updates, and maintains database tables for each object type (via the SMS). The following illustrates preferred but nonexclusive list of primary and derived object types:

Person , S User Customer Category Document Analyst Brief Analyst Report Case Study White Paper Company Profile o E_Book E-Magazine Email Message Email Annotation Email Nears Posting o Email Distributi~n List ~ Email Public Folder Email Public Folder Newsgroup News Article Event Meeting Corporate Event Industry Event TV Event Radio Event Print Media Event Online Meeting Arts and Entertainment Event Online Course Media Book ~ Magazine ~ Multimedia ~ Online Broadcast ~ Online Conference Object types are preferably expresses as hierarchical paths. The path can be extended, e.g., "events\meetings" can be extended with "qualified Meetings,"
e.g., "events\meetings\company meetings." This schema model is indefinitely extensible arid configurable.
Virtual Information Object Typc~s. Virtual Information Object Types are object types that do not map to distinct object ty~~es, yet are semantically of interest to users. An example is the "Customer Email" object type, which derives from the "Email"
object type.
This object type is "virtual" in tliat it does not have a distinct schema and, as a consequence, does not have a distinct table in the SMS on the KIS. Rather, it uses the "Email" table on the SMS, since it derives from the "Email" object type. Even though it 1S Ilot a distinct object type, users will be interested in browsing and searching for "Customer Email"
as though it were indeed distinct.
In the preferred embodiment, Virtual Object Types are implemented by storing the metadata in the appropriate table on the SMS (in this case, the "Email" table, since the object type derives from "Email"). However, the resolution of queries for the object type is accomplished differently from regular queries for distinct object types. When the server SQP
receives a semantic query request (via the: XML Web Service) for a virtual information object type (such as "Customer Email"), ii: resolves the request by joining the tables that together form the object type. For instance, in the preferred embodiment, in the case of "Customer Email," the server will resolve in query with the SQL sub-query:
SELECT OBJECTll~ FROM EMA.IL WHERE OBJECTID 1N (SELECT
OBJECT117 FROM CUSTOMERS WHERE EMAILADDRESS IN (SELECT
EMAILADDRESS FROM EMAIL) 2ss This query corresponds to "Select all objects from the Email table that have an email address value that is also in the Customers table." This assumes that "Customer Email"
refers to email that is sent by or to a customer. Other definitions of the virtual object type are also possible and the query resolution is prei:erably consistent with the definition. The SQP
preferably applies this sub-query to all queries for "Customer Email." This sub-query essentially filters the Email table for those email messages that are from customers. This returns the desired result to the user with the illusion that there is a "Customer Email" table when there really is not.
The present invention contemplates a variety of schemas associated with each object type. Other schemas are in development that will have comparable applicability to the present invention. The "Document" schema, for example, may be extended with fields from the Dublin Core schema (http://www.cis.Ohio-state.edulcgi-bin/rfc/rfc2413.html) and other industry standard schemas. hn yet another example, "News Article" schema may be an extension of the NewsML schema (http:~%www.newsml.org). By way of example only9 preferred user object schema made in accordance with the present invention are shown with reference to FIGURE 29. All schemas preferably have as an identical subset the fields of the Object schema. MailingAddressTypeIDs preferably associated with the User (person) object schema includes those shown with reference to FIGURE 30.
By way of example only, the preferred category object schema made in accordance with the present invention is shown with reference to FIGURE 3-l :v By way of example only, the preferred document object schema made in accordance with the present invention is shown with reference to FIGURE 32. The "DocumentCategory"
field refers to a proprietary category that is tagged with the document (by the document data source) and not to a semantic category managed by the KIS itself. The "DocumentFormatTypeID" field refers to the type of document. The Print Media Type IDs of the preferred embodiment are shomi in FIGURE 33, and the preferred FORMATTYPEID
are shown in FIGURE 34.
By way of example only, the preff;rred email message list object schema made in accordance with the present invention is shown with reference to FIGURE 35.
Email Priorities are preferably 0, 1, or 2, corresl>onding to low, medium, and high priority. The EmaiITypeID preferably includes EMAILTYPEID~EMAIL, EMAILTYPEID NEWSPOSTING and EMAILTYPEID EMAILANNOTATION
(values 1, 2 and 3). Exemplar tables showing the email distribution list and email public folder object scheriias of a preferred embodiment of the present invention are shown in FIGURES 36 and 37, respectively. In the preferred embodiment, the PublicFoIderTypeID
includes tl5ose shown in FTGURE 3S.
By way of example only, the preferred event object schema message list object schema made in accordance with the present invention is shown with reference to FIGURE 39. FIGURE 40 shows the events types of a preferred embodiment of the present invention.
By way of example only, the prefa~rred media object SCheIna lllessa~e list object schema made in accordance with the present invention is shown with reference to FIGURE 41. FIGURE 42 shows the media types of a preferred embodiment of the present invention.
By way of example, FIGURES 43-45 illustrate additional samples showing how objects are categorized and utilized in the preferred embodiment of the present invention.
FIGURE 43 illustrates root object container types. FIGURE 44 illustrates a hierarchical schema for qualified abject types. FTGURE 45 illustrates samples of native container object type predicates. All types except the Person and Customer types preferably inherit all predicates from the root type "All Information." The present invention provides for native container object type predicate templates, for example including for: All;
Breaking News;
Categorization; Author; Amiotations; Definite Links; Probabilistic Links; and Popular.
b. Semantic Data Gatherer In the preferred embodiment, the Semantic Data Gatherer (SDG) is responsible for adding, removing, and updating entries in the Semantic Network via the SMS.
The SDG
consists of a list of XML Web Service references. These form an Infornlation Source Abstraction Layer (ISAL). Each of these references is initialized to gather data from via a Data Source Adapter (DSA). A data source adapter is an XML Web Service that gathers inforniation from a local or remote semantic; data source for a give object type. It then returns the XML corresponding to object entries at the data source. All DSAs preferably support the same interface via which the SDG will gather XML data. This interface includes methods to:
~ Retrieve the XML metadata for objects for a given start and end index (e.g., objects 0 through 49).
~ Check whether there any objecla have been added or deleted since a particular date/time (on the DSA's time clack).
o Fetch the XML metadata for objects added or deleted since a particular date/time (on the DSA's time clock) ~ Check whether an object still e~;ists in the semantic data source - by examining the XML metadata for the object (passed as an argument) If each call to the DSA XML Web Service will be stateless, the API should' include information, preferably via a string with command parameters,which qualifies the request For example, a DSA for.an email inbox includes parameters such as the name of the user whose inbox is to be gathered. A DSA for a. Web site or document store will have to include information on the URL or directory path to be crawled.
Each DSA is required to retrieve infornlation in the schema for its object type.
Because a DSA must be implemented for a particular object type, the SDG will expect XML
for the schema for that object type when it invokes a gather call to the DSA.
25s The SDG is responsible for maintaining the integrity and consistency of all the database tables in the SMS (the Semantic Network). In this embodiment, the SDG
is also referred to as a Semantic Network Manager (SNM). The database tables preferably do not contain redundant or stale entries. Because the SDG retrieves objects with well-known schemas the semantics of each of the object types is understood, 'and the SDG
maintains the consistency of the tables accordingly. F'or example, the SDG preferably does not add redundant Document XML metadata to the DOCUMENTS table. The SDG uses the semantics of dacuments to check for redundancy. In the preferred embodiment this is accoiilplished ,by comparing the author name, creation date/time, file path, etc. The SDG also performs this check for other tables (e.f;., EVENTS, CUSTOMERS, NEWS, etc.).
For example, the SDG W 11 perform redundancy checking for events by examining the title, the location, and the date/time. Other tables are maintained accordingly. The SDG
will also update objects in the database tables that have been changed.
The SDG is also preferably responsible for cleaning up the database tables.
The SDG
periodically queries the DSA to determine whether all of the objects in each table managed by the DSA still exists. Far example, for a DSA that retrieves documents, the SDG will pass the XML metadata to the DSA Web service and query whether the object still exists. The DSA attempts to open the URL for the document. If the document does not exist anymore, the DSA will indicate this to the SDG. Individual DSAs, and not the SDG, are responsible for object validation to avoid security restrictions that are data source specific. For example, there might be data source restrictions that prevent remote access to local resources. In such a case, only the DSA XML Web Service (which is preferably running locally, relative to the data source) will have access to the data source. Alternatively, some DSAs might run on the Agency server, alongside the SDG and other server components, and retrieve their data remotely.

Having the DSAs handle object validation also provides additional efficiency and ..
security in that the DSA prevents the SDG from knowing the details of~how to open each data source to check whether an object still exists. Since the DSA needs to know this (since it retrieves the XML data from the data source and therefore has code specific to the data source), it is more appropriate for the DSA: to handle this task.
The SDG preferably maintains a gather list that will point to DSA XML Web Service URLs. The ICIS administrator is able to add, delete, and update DSA entries from the SDG
gather list. Each gather list entry is preferably configured wish:
1. The name and XML Web Service reference of the DSA. This essentially will refer to a combination of the data source, the object type, and a reference to the XML
Web Service that implements the DSA (e.g., via a WSDL web service URL).
Examples include:
a. Microsoft Exchange 2000 Email DSA. This DSA will gather email XML
metadata froze a Microsoft Exchange 2000 Inbox or Public Folder b. Microsoft Exchange 2a~00 Calendar DSA. This DSA will gather event XML metadata from a Microsoft Exchange 2000 Calendar c. Microsoft Exchange 2000 Users DSA. This DSA will gather users/people XML metadata from a Microsoft Exchange 2000 Directory d. Microsoft Exchange 2000 Email Distribution List DSA. This SDA will gather email distribution list metadata from a Microsoft Exchange 2000 Directory e. Lotus Notes Inbox. This DSA will gather erriail XML metadata from a Lotus Notes Inbox or Public Folder ~ Siebel CRM Database. This DSA will gather customer XML zri~etadata ~ from a Siebel CRM system " , g. Web site. This DSA will gather document XML metadata from a Web site h. File Directory or Share. This DSA will gather document XML metadata from a file directory or share i. Saba E-Learning LMS Repository. This DSA will gather. E-Learning XML metadata from a Saba Learning Management System (LMS) repository j. Microsoft Sharepoint Document DSA. This DSA will gather document XML metadata from a Microsoft Sharepoint server workspace k. Reuters News Repository. This DSA will gather News Article XML
metadata from a Reuters news article repository 2. The description of the DSA gather entry.
3. A string indicating initialization. information for the DSA.
4. The gather schedule - this indicates how often the SDG should 'crawl' the DSA
to gather XML anetadata.
In a preferred embodiment, the Agency is initialized with a user directory domain and group niame. In this case, the SDG preferably automatically enters a gather list entry for the user directory DSA. For example, if the Agency is configured...with a Exchange 2000 User Directory with Domain Name "Foo" and Address Book or group name "Everyone,"
the SDG
creates a gather list entry with the Exchange 2000 Users DSA (initialized with these parameters). Alten~atively, the Agency can be configured to obtain its user directory from any email application server (e.g., Microsoft Exchange or Lotus Notes). The SDG initializes gather list entries ~~rith an Email Inbox and Calendar DSA for the system user (and Email Knowledge Agellt, described below). These three gather list entry DSAs (Users,.Inbox, and Calendar) are initialized by default. The Inbox is preferably used to store Agency email postings and annotation and the Calendar DSA is used to store events posted to the Agency by users. Other custom DSAs can be added by the Agency administrator.
The SDG also keeps track of the la~;t time the SDA reported to it that objects have been added or deleted to or from the data source. This dateltime information is preferably based on the SDA's clock. Each time the SDA reports that there is new or deleted data, the SDG will update the dateltime information in its entry for the SDA and gather all the new or deleted information in the SDA. The SDG will then update the database tables.
The SDG preferably maps the XML information it receives from the SDAs to the Semantic Network of the present invention. The SDG stores all the XML metadata in the database tables in the SMS. W addition, the SDG parses the XML it receives from the SDA
and, where necessary, IIlapS Se111at1t1C links to specific XML fields. The SDG
adds or updates semantic links .in cases where the XML 'includes information that "links"
objects together.
For example, the schema for an email object preferably includes fields including "From,"
"To," "Cc," "Bcc," and ''Attachments." In the case of the "From," "To," "Cc"
and "Bcc"
columns, the fields in the XML refer to ernail addresses (separated by delimiters such as ' ;"
or "," or a space). In the case of the "Attachments" column, this field will refer to the file paths of the tiles that are attached to the ernail message (separated by delimiters such as ",").
This raw XML is stored in the EMAIL database table, along with the other columns. Iri addition, the SDG parses the fields of the email object and adds semantic links to other objects that are .identified by the content; of those feelds. For example9 if the "to" field contains "JOhIl C! fOfl.COITI" and the attachments field contains the string "c:\foo.doc, c:\bar.doc," the SDG will process the email as follows:
1. Find any ohjlect in the USERS table with the email address "john~foo.com."
Also, search for other USER objects with email addresses in the FROM, T0, CC, and BCC fields.
2. If anyobjects are found, add a semantic link entry to the SEMANTICLINI~S
table with the email object id as t:he subject and the appropriate predicate type id.
In this case, the predicate PREDICATETYPEID~CREATOR refers to the originator of the email message. 'The predicate PREDICATETYPEIDySENTTO
is used to link the email object and the USER objects referred to by the contents of the "to" field in the email XM:L metadata. The predicate PREDICATETYPEID COPIEDTO and P~EDICATETYPEID BLINDC~OPIEDTO are used to link objects in the "cc"
and "bcc" Eelds in similar fashion.
In the case of attaclunents, the SDG extracts the XML metadata for the attached documents. If an XML object with the file path already exists in the SMS (or, in other words, the Semantic Network), the SDG will update the metadata. If the XML object does wot already exist, the SDG creates a new document object with the XML metadata.
The SDG

will adds an entry to the SEMANTICLINKS table. with the email object ID as the subject, the new document's object ID as the subject, and the predicate PREDICATETYPEID ATTACHEDTO. This allows the user to be able to navigate from an en pail message to its attachments and then use the attachments as pivots to continue to browse the Semantic Network, for example using semantic tools like the Smart Lens (discussed below).
The SDG does not create any objects in the event for which it does not find user objects that match the entries in the XML fields. Preferably, the SDG gathers information from a Directory SDA when a user is; manually added to the Agency. The Agency administrator preferably adds users, to the Agency via the user group on the Agency properties. ,.
The following illustrates an example of mapping raw email XML metadata to the Semantic Network.
<email from="john@foo.com"
to=g°nosa~nez-~rana.net°' cc-6'steve car nervana.net"
bcc-"patrick cr nervana.net"
subject="Meeting this Friday"
body="Let us meet an Friday at 2pm"
attachments="c:\foo.doc; c:\bar.htm" >
</email>
is converted to the object graph illustrated in FIGURE 46.
c. Semantic Nehvork Gor~sistency Checker .
The Semantic Network Consistency Checker (CC) complements the consistency checking that is performed by the SDG. As described above, the SDG maintains the integrity of the database tables by precluding the addition of redundant entries into the Semantic Network (from various data sources). The CC also ensures the consistency of the OBJECTS
and SEMANTICLINKS tables. The CC periodically checks the OBJECTS table to ensure that each object exists in the native table (preferably by checking the OBJECT>D field value). For example, a document object entry in the OBJECTS table preferably also exists in the DOCUMENTS table (with the same object ID). The CC removes any object in the OBJECTS table without a corresponding object iii the native table (DOCUMENTS, S EVENTS, EMAIL, etc.) and vice-versa.
The CC is also responsible for maintaining the COIISISteIICy of the SEMANTICLINKS
table. The semantics of this table are preferably as follows: A semantic link cannot exist if either its subject ("linked from") or its object ("linked to") do not exist.
To illustrate this, if object A links to object B with predicate P, and either A or B is deleted, the link should be deleted. The CC periodically checks the SEMANTICLINKS table. If any of the subjects or objects has been deleted, the CC deletes the semantic lime entry.
Consistency checks may be implemented in code in the KIS itself or as stored procedures or constraints at the database level.
d. Infea-enc~ Engine I S The Inference Engine is responsible for adding semantic links to the Semantic Network. The Inference Engine employs Inference Rules, which consist of a set of heuristics, to add semantic links based on ongoing semantic activity. The Inference Engine is preferably .;
allowed to remove semantic links. Decision Agents (described below) use the Inference Engine to assist knowledge-workers in:making decisions.
The >iiference Engine. operates'by mining the SeInaIltlC Network and adding new semantic links that are based on probabilistic inferences. For example, the Inference Engine preferably monitors the Semantic Network and observes patterns in how ~ email is sent, the type of email sent and by whom. The inference Engine infers from this information background information, such as the expertise of the user, related to various subject matter 2S categories within the monitoring purview of the Inference Engine. For example, the Inference Engine adds semantics links with the predicate PREDICATETYPEID EXPERTON to indicate that a user is an expert in a particular category. The subject in this case will be a user object and the object will be a category object. To infer this, the Inference Engine is preferably configured to observe semantic; activity for at least a certain period of time (e.g., two weeks), or to.only infer links after users have sent at least a certain predetennined number of messages or authored a certain number of documents. The Inference Engine infers the new link by keeping statistics on the PREDICATETYPEID CREATOR and PREDICATETYPEID CONTRIBUTOR links.
By way of example, the Inference Engine may infer that users are an expert on a category if:
~ Of all categories of email messages they have written, this category is one of the top N (configurable).
o They have written email messages on the same category an average of M times or more per week (configurable).
~ They have written at least O email messages (configurable) in the past P
111011ths 1 S (configurable).
h/tore sophisticated infez°ence models ovith which to accurately infer this data are contemplated. For example, probability distributions as well as statistical correlation models rnay be employed. Preferably these models will be developed on a per-scenario basis over time.
The Inference Engine is also responsible for removing links that it might have added.
For example, if an employee changes jobs, he or she might "cease" to be an expert on a specific category (relative to other employees). Once the Inference Engine detects this (e.g., by observing email patterns), it removes semantic links that indicate that the person is an expert on the category.
Inferred semantic links are important for scenarios that involve probabilistic semantic queries. For example, in one embodiment of the present invention, using the Information Agent,..users may drag and drop a document from,their file-system onto an Agent (say, People.Research.All). In this case, users will want to know the people in the Research department that are experts on the document. The browser will then invoke an SQML query with the Agent as resource (or subject), 'the predicate nervana:experton, and the document path as the object. The Presenter will then retrieve the XML metadata for the document and call the XML Web Service, residing on the Agency that hosts the Agent, with the predicate ID and the document's XML metadata as arguments. The server-side semantic query processor on the Agency processes this XML Web Service call and translates the call to a SQL query consistent with the data model of the Semantic Network. In this example, the call is preferably resolved as follows:
1. For all semantic domain entries in the KDM, call the corresponding KBS to categorize the document.
2. Map the returned categories to cafegory objects in tlae Semantic Network (by comparing URLs) 3. Invoke a query using the query of the People.Research.All Agent as a sub-query.
In this example, the final query appears as follows:
SELECT ~ FI~OI~ TJSERS WHEI~LE DEP~.R'Tl~/IEhIT LIhE °~RESEARCI-Ig' AND OBJECTID IIV (SELECT OBJECTff~ FROM SEMANTICLINKS
WHERE OBJECTTYPEID = 32 AND PREDICATETYPEID = 98 AND
SUBJECTID IN (SELECT OI3JECTID AS SLTBJECTID FROMr' CATEGORIES WHERE OBJECTID IN (34, , 5G, 78)) 'AND
LINKSCORE >. 90 ) This query assumes that the object type ID :for the user object type is 32, the predicate type ID value for PREDICATETYPEID EXPER'TON is 98, the document belonged to categories with the object ID 34, S6, and 78 and that the semantic link score threshold is 90.
e. Server-Side Semantic Query Processor The server-side Semantic Query Processor (SQP) responds to semantic queries from clients of the KIS. The SQP is preferably the main entry point to the Semantic Network on the KIS (or Agency). The SQP is exposed via the Agency's XML Web Service. The SQP

processes direct Agent semantic queries and generic (client-generated) semantic queries with semantic link filters (see below). For quE;ries with server-side Agent filters, the Information Agent passes the Agent name and object index arguments to the SQP to be invoked. For example, the browser may ask for objects 0-24 on Agent Documents.Teclmology.Wireless.All. In this example, the SQP looks up the Agent query in the Agents table and invokes the query onto the database that hosts the Semantic Metadata Store (SMS). The~Agent query is pref i-ably stored as SQL or another well-known query format like XQuery or XQL. The SQP may convert the query fornlat to a format that the database (that holds all the tables) understands. Because most commercial databases understand SQL, it will preferably operate as the default Agent query format.
The Agent query preferably follows th~-query rules described above. Therefore, the query returns the object ID rather than the schema fields for the Agent's object type. In the above-described example, Documents.Tec;hnology.Wireless.All invokes the Agent query "SELECT .OBJECTID FROM DOCUMENTS WHERE ..." The SQP is responsible for issuing a query that is filtered with the Agent query, but which retun~s the actual metadata for the object type (in this case, the "document" object type). In this example, the query appears as follows:
SELECT * FROM DOCUMENTS WHERE OBJECTID IN (SELECT
OBJECTID FROM DOCUMENTS WHERE ...) This query returns the data columns for the "document" schema For all the objects with an object ID that matches those in the original Agent query. The SQP
reviews the metadata results of the database query and translates them to well-formed XML
using the appropriate schema for the object type of the Agent (in this case, "document"). In the event that the database supports raw XML retrieval, the SQP optimizes the query by asking the database to give it XML results. This results in better performance since the SQP does not have to perform the extra translation step. The SQP passes the XML back to the caller via the Agency's XML Web Service.
The SQP preferably handles more complex queries that are passed by the semantic browser,(or other client of the XML Web Service). By way of example, such queries may take the form of the following XML
Web Service API:

String InvokeSemanticQuery( Integer BeginIndex, Integer Endh~dex, String AgentName, ' Integer NumberOfLinks, String OperatorNames[], String LinkPredicateNames[], String LinkTypeNames[]

1S String LinkObjects[]);

In this example, the "[)" symbols refer to arrays. The API takes a zero-based begin index, a zero-based end index, an optional Agent name, an integer indicating the number of semantic links, an array of operator names, an array of lime predicate names, an array of link type names, and an array of strings that refer to the link objects. If the Agent name is NULL (""), the SQP processes the query "as is"; without any preconceived Agent filter.
This will be the case with queries that are wholly generated form the client. The arrays are variable sized because the "NumberOfLinks" parameter indicates the size of each array. The operator names include valid predetermined operators, including logical operators, which can be used to qualify queries in SQL or other query formats. Examples include tenn:or and tem~:and.
The ~ link predicate names may include one or more predefined predicates , (e.g., :.
tenn:relevantto, term:reportsto, termaentto, tenn:annotates, term:annotatedby, terni:withcontext, etc.).. The link type names indicate the type of link objects. Common examples include term:url and term:object. In the case of term:url, the link object string refers to a well-formed URL comprising objects://.. , or Agent:!/.... In the case of teen:object, the argument will be a well-formed XML metadata instruction referring to a object defined within the present invention. This object is preferably resolved from the client or from another Agency. The API returns a string that contains the XML results (in addition to the return value for the XML Web Service method call itself.
By way of example, the SQML with the data:
<resource type---"term:url"
Agent:/fall.criticalpriority.all@abc.com/Agency.asp>
clink predicate="terni:relevantto"
type----"tenn,:obj ect"
object:!l4576 >
</1 ink>
clink operator--"or"
predicate--"term:intersects"
' type--"tenn:url"
Agent://email.wireless.all cr abc.comlAgency.asp>
</link>
</resource>
is resolved on the Agency located at the Web service on abc.com/Agency.asp to:
2p InvokeSemanticQuery( 0, 24, "all.criticalpriority.all", 2, f "tenn:and", "term:or" }, { "term:relevantto", "term:intersects" }, { "term:object", "tenn:url" }, { "object://4576", "Agent://email.wireless.all@abe.com/Agency.asp" } );
This is preferably resolved to a SQL query:
SELECT TOP 25 * OBJECTS WHERE OBJECTID IN (SELECT
OBJECTID FROM OBJECTS WHERE CREATIONDATETIME='02/26!02' AND (OBJECTID [RELATEDTO] [OBJECT WITH ID 4576]) AND
OBJECTID IN (SELECT OBJECTS FROM EMAIL WHERE CATEGORY
[IS] 'WIRELESS') This SQL example uses shorthand to illustrate the type of query that will be generated by the SQP. The SQP retrieves the XML and returns it to the caller. ThIS XML is in the form of SRML (or Semantic Results Markup Language), which is the XML meta-schema definition for sela~antic query results in the preferred embodiment of the invention.
Sample A shown in the Appendix hereto is a sample SRML semantics results buffer or document.
This is a sample of tile XML that an Agency return,> in response to a semantic query:
The client Skin takes these results and generates presentation form them (using XSLT and/or script), based on the properties of the Skin and the Agent (object Skin/Context Skirl/Blender Skin), the amount of display area available, disability conslderatloll5 and other Skin attributes.
f: NatirralLangzragePcnser The Natural Language Parser (NLP) preferably converts natural language text to either an API call that the SQP understands or to raw SQL (or a similar quer~r format) that can be processed by the database. The Natural Language Parser is passed text directly from the semantic browser or by email via the En;~ail Knowledge Agent (see below).
g. Ernail Knowledge Age-.nt The KIS preferably includes one primary publishing component, referred to as the Email Knowledge Agent (or Enterprise Information Agent (EIA)). This Agent functions, in essence, as a digital employee, and preferably includes a unique email address (e.g., a custom name selected by the Agency administrator). The Email Knowledge Agent complements existing publishing tools such as Microsoft Office, SharePoint, etc. by adding a "Fire and Forget" InethOd of publishing information a:nd sharing knowledge. This is especially useful in cases where the person publishing the infomnation does not know who might be interested in it:

In a preferred embodiment of the present invention, users send email to the Email Knowledge Agent to publish comments, amotations, documents, attachmeilts, etc.
The Email Knowledge Agent extracts meaning from the email and properly adds it to the Semantic Network. Other users are able to access published information via Agents of other platform presentation tools such as drag and drop, tine Smart Lens, etc. (discussed below).
The Email IW owledge Agent 1S a Sy5te111 C0111pO11eI7t that is created by the Agency administrator. The system user name is indicated when the server is first installed. The system user preferably corresponds to an email user in the enterprise email system (e.g., Microsoft Exchange, Lotus Notes, etc.) In this embodiment, the Email Agent has its own mailbox, calendar, address book, etc. These in turn correspond to the objects on the Email Server for the system user. When the server is installed, the KIS installs the appropriate DSA
for the system inbo~ (depending on the email application). The KIS preferably automatically adds a gatherer list entry in the SDG indicating that the system inbox should be periodically crawled for email.
>3ecause the Email Knowledge Ager~t is a first-class email addr~ssq it als~~
ser~re.~ as a notification source and a query source (for natural-language and instant messaging).
Notifications from an Agency are preferably sent by the Email Knowledge Agent (indicating that there is new and relevant information the user might be interested in, etc.). The Email Knowledge Agent may also receive email from users as natural language queries.
These messages are parsed by the SQP and processed. The XML results are preferably sent to the user as an HTML file (with the appropriate default Skin) generated with XSLT
processed over the XML results of the natural-language query.
Because the Email Knowledge Agent is a regular familiar component or "employee," ' the Agency administrator preferably adds the address to distribution lists.
This step allows the SDG to semantically index all the email in these distribution lists, thereby populating the Semantic Network by seamlessly integrating the Email Knowledge Agent into distribution lists useful to users. This is a very seamless way of integrating the digital Information Nervous System of the present invention with the way people already work in an organization.
Annotations. The Email Knowledge Agent is preferably used to publish alu~otations.
In the present 111Ve11t10I1, almotations are preferably email messages. In the preferred embodiment, the annotation object type is a subclass of the email object type.
This allows usersvto use email, typically the most common publishing tool, to amiotate objets in the semantic.browser. Users are able to amlotate objects and add attaclunents to the annotations.
These attachments are semantically indexc°d by the SDG on the KIS. This makes possible scenarios where a user is able to navigate; from, say, a document, to an annotation, to its document attachment, to an article on Reuters to an industry event that starts next week.
The process described for semantically indexing email (by mapping the email XML
schema to the Semantic Network) also applied to annotations. However, in the case of annotations in a preferred embodiment of the present invention, additionally processing is 1 S desirable. Specifically, evhen the user clicks "Annotate" on an object in the Presenter window in the semantic browser (described below), the browser loads the registered email client on the local machine (e.g., Microsoft Outlook, Microsoft ~utlook Express, etc.).
The "to"' field is populated with the address of the systerrl user for the Agency that hosts the object. The subject field is populated with a special string, fox example, "annotation:
object=[objectid)".
When the email arrives in the Email Knowledge Agent's inbox, the DSA for the email inbox will pick it up (e.g., via a server event). Tlle SDG retrieves the new email XML metadata from the DSA by receiving an event, or from the DSA the next time it asks the DSA for more data. In a preferred embodiment, this polling process occurs frequently. The DSA returns the XML metadata of the email object, oblivious to the fact that the email object refers to an email object type or an annotation object type. The SDG processes the email XML metadata, and examines the "subject" field. If the SDG "sees" the "annotation:" prefix, it knows that the email is actually an annotation, and proceeds to extract the object ID
argument from the siibject text. The SDG updates the Semantic Network for remaining email messages (adding each message to the OBJECTS and EMAIL tables, adding semantic links for the "from,"
"to," "cc," "bcc," and "attachments" fields, where necessary, etc.). ~W the preferred embodiment, the SDG performs an extra step. Specifically, it adds a semantic link entry that links the email object with the object indicated by the object ID argument in the subject text (with the PREDICATETYPEID_ANNOTATES predicate).
With the present invention, an annotation is treated as another semantic link with a special predicate. As a result, all the semantic features apply to amiotations, such as semantic navigation via semantic links, semantic queries, etc. For example, a user can query for all annotations written by every member of his of her team in the last six -months. This can be accomplished in the semantic browser by dragging, for example, the Agent Annatations.All on top of the Agent People.MyTeam.All and then sorting the results, or by creating a Smart Agent, which in turn invokes the "Create Smart Agent" wizard to create the query.
1 S Via. ~~~a~v~l~eP~c ~~D21CIPDt ~.vrxe~crgm°
The Knowledge Domain Manager is the component on the KIS that is responsible for adding and maintaining domain-specific inl:elligence on the Semantic Network.
The KDM
essentially "annotates" the Semantic Net~,vork with domain-intelligence. The KDM is initialized with URLs associated with one or more instances of the Knowledge Base Server (KBS), which in turn effectively stores "knowledge" for one or more semantic domains. The KBS has ontology and categories corresponding to taxonomy for each semantic domain that it supports. In addition, an Agent with a semantic domain (connected to a KBS) responds to semantic queries. If an Agent does not belong to a semantic domain, it cannot correspond to semantic queries (that' require an ontology or taxonomy). Rather, it only responds to keyword-based queries (albeit it will still provide context and time-sensitive retrieval services, but the available contexts will be limited).

Each entry in the KDM is a semantic domain entry. The semantic domain entry has tile URL to the KBS and a semantic domain name. The semantic domain name maps to a specific ontology on the KBS. In the preferred embodiment of the present invention, semantic domain names follow the convention:
<Top Level Domain Name>\<Secondary Level Domain Name>......
Examples of semantic domain names include:
~ Industries ~ Industries\Phannaceuticals\LifeSciences ~ Industries\InformationTechnology ~ General\Sports.Basketball\NBA
General\Sports.Basketball\CBA, Alternatively, semantic domains names can be referred to as "domain paths" as long as they are fully qualified. Full qualification is achieved by adding an Internet domain name prefix to the beginning of the path. This indicates the "owner" or "source.'-' of the semantic domain. For example, "Nervana.NET\Industries\I'hannaceuticals" refers to "Industries\Phannaeeuticals" semantic domain according to the "NE1~~~A~TA,NET'9 Internet domain name. In another example, "Reuters.com\Sports\Basketball" refers to "Sports\Basketball" on "Reuters.com." Using this approach, domainrt\names and paths are ~ maintained globally unique.
The Knowledge Domain Manager (KDM) periodically requests each KBS in its domain entry list for the categories in tl-~e knowledge domain. The KDM is preferably implemented as an XML Web Service on the KIS. The KDM includes configuration options for each semantic domain entry. One of theae options may iyclude the schedule with which the KDM will update the Semantic Network with domain-specific intelligence corresponding to the semantic domain entry. For example, the Agency administrator may configure the KDM (via the KIS) to crawl a semantic domain on a KBS every day at lpm. The update schedule ,should be. consistent with hover often the administrator believes the ontology or taxonomy on the KBS changes.
The KIS preferably invokes the KDM periodically and asks it to update the CATEGORIES table. In the preferred embodiment, the KDM calls the KBS (via an XML
Web Service API call) to obtain updateei categories for the semantic domain name in the semantic domain entry, which corresponds to a particular taxonomy. An example of an API
call follows: GetCategoriesForSemanticDomain (String SemanticDomainName). The KBS
returns an XML-based list of all the categories in the semantic domain referred to by the semantic domain name: This XML list is consistent with the CATEGORIES schema shown above (category URL, name, description, the KBS URL and the semantic domain name). The KDM updates the CATEGORIES table with this information. For category entries that already exist in the table, the KDM updatca the name and description. For new entries, the KDM requests a new object ID from the object manager and assigns that to the category entry. Since, in the preferred embodiment, a category is an "object," it inherits from the 1~ Objeca type and therefore has an object ID.
The KDM synchronizes the' CATEGORIES table to the CATEGORIES list on the KBS (for a particular semantic domain) by deleting entries in the CATEGORIES
table not present in the new list after examining the URL of the category entries and obtaining the relevant KBS URL and semantic domain name. If a semantic domain entry is deleted from the KIS, the KDM deletes all category entries with a correspondipg semantic domain name .
and KBS URL. Essentially, this will be akin to ridding the Agency of existing knowledge.
The KDM periodically categorizes all "knowledge objects" in the Semantic Network based on its semantic domain entries. When new objects are added to the Semantic Network by the SDG, the SDG requests that the KDM categorize the objects. The KDM
enumerate all KBS instances in its semantic domain entries and invokes XML Web Service calls with the 27s XML of the object as the argument. In the preferred embodiment, the KBS
returils a result in an XML buffer similar to:
<results>
<result categoryurl="category:/Ifoo" ' score--"91" >
<result categoryurl-"category://bar"
score="93" >
<result categoryurl="category:!/foobar" ' score=" 100" >
</results>
This information indicates the semantic categorization weights of the XML
object for the categories in the semantic domain on t:he KBS. In a preferred embodiment of the present invention, the semantic domain entry is initialized with a threshold (0-100) indicating the minimum weight that the KDM should request from the KBS. The KBS returns scores that exceed the predeterniined threshold. The KDM annotates the Semantic Network based on these categorization results. This is pre:Ferably accomplished by adding or updating a semantic.link with the predicate type ID of "belongs to category" with the object ID ~f the category in the result. The KDM will update the SEMANTICLINKS table. 'Assuming by way of example that the object that is categorized has an object ID value of 56, the update query appears as follows:

OBJECTll~=56 AND PREDICATETYPEID = 67 AND SUBJECTID IN
(SELECT OBJECTID AS SUBJECTID FROM CATEGORIES WHERE
URL LIKE "CATEGORY://FOO") The KDM periodically scans and categorizes all the "knowledge objects"
(documents, news articles, events, email, etc., preferably not including objects like people)..
This process preferably occurs even if an object in the Semantic Network has previously been categorized as the KBS might have become "smarter" and therefore provides superior categorization. In such a case, the results could change even if the same categorization request is repeated. This will occur, for example, if the ontology on the KBS
has been updated. Thus, in the preferred enlbOd1111ent, categorization will be perfornled both when an object is added to the Semantic Network by the Semantic Data Gatherer and periodically to S ensure that the Semantic I~Tetwork has the most up-to-date domain knowledge.
i. O.tlrer Components The Favorite Agents 1\~anager. On Agencies that support User States, a Favorite Agents Manager manages a list of per-user favorite Agents. h the preferred embodiment, the Favorites Agent Manager stores a mapping of user names to favorite Agents in a UserFavoriteAgents table C'~mp~und Agent IVgannger. A Compound Agent Manager W anages the creation, deletion, and update of compound Agents. As described above, compound Agents are Agents that are comprised of other Agents in the system, and are initialized to return the union or intersection of the query results in the contained Agents. The Compound Agent Manager n Manages all compound Agents in the system and leaps compound Agents to the Agents they contain via the ColnpoundAgentMap table.
The Compound Agent Manager exposes functions to create compound Agents, delete, rename, add to and remove Agents from them, and indicate whether a union or an intersection is desired. Compound Agents can be added to other compound Agents. On invocation, the semantic query processor asks the Compound Agent Manager for its compound query. The Compound Agent Manager navigates through its Agent map graph and returns a complex query of all the queries of all Agents that it contains. If Agents are deleted, compound Agents "pick up" the new state when they are invoked, ignoring the Agent query.
In other word's, the Compollndlllg of queries is only done for Agents that still exist. If the compound Agent observes that one of its Af;ents has been deleted, it will delete the entry from its map.

User Profile Manager. The User Profile Manager (UPM) preferably uses the Inference Engine to infer the user's profile on an ongoing basis. The UPM
annotates the Semantic Network based on feedback from users as to 'their explicit preferences. In the preferred embodiment, this process involved use of the PREDICATEID
ISINTERESTEDIN
predicate. The UPM infers semantic links and amotate the Semantic Network with the PREDICATEID ISLIKELYTOBEINTER:ESTEDIN predicate. All query results to the user will be qualified (out-of band) with a query to the Semantic Network for the PREDICATEID_ISLII~ELYTOBEINTERI~STEDIN predicate. Query results are based on the user's habits, as the Inference Engine learns them over time.
Alternatively, the UPM may be configured with user profile information stored in the User State Store (USS). This is infonnati~on manually entered at the client indicating the user's preferences. This information is transferred and stored at the server that the user is interacting Wlth. These preferences are tied to different schema. For example, for documents, the schema may be based on the preferred categories. For email messages, the schema may 1 i be based on preferred categories, authors, or attachments. These are two of many possible examples. The UPS annotates the Semantic Network based on the manually entered information in the USS.
Server Notification Manger. The Server Notification Manager (SNM) is responsible for hatching server-side notifications and forwarding them to users. Ill, the preferred embodiment, users register for se3:-ver-side notifications at the Agent level. Each Agent is capable of firing notifications o~f its query results. The Server Notification Manager determines how to filter the query results and format them for delivery via email, voice, pager or any other notification mechanism, e.g., the Microsoft .NET Alerts notification services. The Server Notification Manager maintains information on the last time users "read" the notification. This is preferably indicated from the client via a user interface. The 27s SNM preferably only notifies a user whe;n there is new information on the Agent since the last "read" time for the particular user.
Agent Discovery. Using nairlticast-based Agent discovery, each Agency sends multicast announcements indicating its presence on the local multicast network. The Agency administrator sets the multicast TTL. The present invention preferably uses either use the Session Announcement Protocol (SAP) with a well-known port of 9875 and a TTL
of 255, or a proprietary announcement port with a customizable TTL. For details on SAP, see http:l/sunsite:cnlab-switch.ch/ftp/doc/standard/rfc/29xx/2974, which is incorporated by reference.
The Information Agent preferably includes a listener component that receives S.~AP
announcemelts. In the preferred embodiment, the announcements are sent as XML
and will include the following information ..
~ The server ID (this is a unique icientif er) ~ The server'URL (this is the HTTP URL to the Agency's XML Web Service) ~ 'The announcement period (T) - t111s Indicates the time bete~reer~ each announcement ' .
o Whether there are any new Agents in the Agency since the last announcement and , the last Agent creation time (on the Agency's clock) Each Agency sends the XML announcement and uses Forward Error Correction (FEC) or Forward Erasure Correction to encode the packet. This makes the system robust to dropped packets. Alternatively, the Agc,ncy can be configured to send the XML
announcements several times in succession (per announcement).
The Information Agent multicast listener exposes directory-like semantics to the Semantic Environment Manager. The listener aggregates all the XML
announcements from the Agencies from which it receives announcements. It will also cache the last time it received an announcement from each Agency. The listener flags Agencies that it thinks might be dead or inactive. It does this when it has not heard from the Agency for a time longer than the Agency's announcement period. The listener might be configured to wait for several periods before flagging the Agency as inactive. This will handle the case of dropped announcements (due, perhaps, to traffic congestion). The listener will update the Agency list in the Semantic Environment Manager each time it receives amiouncements, The Semantic Environment Manager periodically inquiries of the listener whether there are any new Agents. The Semantic .Environment Manager checks the Agency list and asks each Agent that is active whether it has new Agents. The Semantic Environment Manager qualifies this request with the Agency's last Agent creation time maintained locally and the current time based on the Agency's clock. The Agency responds and also sends the new value of the last Agent creation time. The Semantic Environment Manager caches this value in the Agency entry: If there -are new Agents, the br~vvser inform the user via a dialog box and asks the user whether he or she wants to view the new Agents.
The present inventioyalso supports Agency announcements using a peen-to peer Agent CIISCOV~I'y. In this model, announcements are sent either to a directory server that all 1 ~ clients check or directly to the clients aria a standard peer-to-peer publishing protocol.
FIGURES 47-53 are exemplar screenshots showing aspects of Agent management by the KIS. FIGURES 47-50 illustrate a sample KIS Agency administration manager showing server-side Agent views and server-side Agents. FIGURE 51 further illustrates sample administration user interface elements for managing SDG (crawl) tasks, system tasks (e.g., the Inference Engine), the system Agent Email (e.g., inbox), calendar and contacts DSA and all the SMS data tables (objects, semantic links, categories, etc.). FIGURE 52 illustrates a sample. of the "Server -Properties" dialog of the present invention in the KIS
Agency administration manager. The dialog illustrates how the server administrator can set~server properties such as the server name, the display name, the SMS Data Store.properties, the KDM properties (e.g., the knowledge domain path) arid the user DSA properties.

illustrates a sample of the "Server Statistics" dialog in the KIS Agency administration 2so manager of the preferred embodiment. The dialog illustrates the display of statistics such as the total number of server-side Agents (Standard Agents and. Blenders), the total number of server-side Standard Agents, the total number of server-side Blenders, the total number of server-side Agent-views, the total number of server-side Agent subscriptions, the total number of infonnati'on objects stored on the server, the total number of semantic links, the total number of users on the server (Agency) and the total number of user groups.
3. IC1V0 if'LEDGE $ASE SER I'ER
The Knowledge Base Server (KBSI is the server that hosts knowledge for the KIS. In most applications, many instances of the KIS will be deployed, but only few (or one) KBS
will be deployed for any given organization. This is because KBS can be reused (they are domain-specif c but data-independent). For example, a pharmaceutical firm might deploy one KB,S initialized with a pharmaceuticals ontology, but have several KIS
installations; perhaps per employee division or per employee group. The KIS preferably includes the fbllowing components:
1 ~ 1. One or more ontologies that correspond to one or more semantic (knowledge) domains. A semantic domain is referred to using a semantic domain name. This is a name that refers to a domain path within a semantic hierarchy. Examples are Industries.Teclwology, Industries.Phannaceuticals.LifeSciences, and General.Sports.Basketball. These names or paths may also be globally and uniquely qualified (e.g., with Internet domain names) as previously discussed.
2. One or more taxonomies that correspond to the supported semantic domains.
These taxonomies contain a hierarchy of category names.
3. A categorization engine that take a piece of text or XML and the semantic domain name.with which. the categorization is to be performed, and returns the categories in that domain that the text or XML belong to, along with the categorization scores (on a scale of 0-10 or, preferably, 0-100).
4. An XML Web Service that exposes APIs to add new supported semantic domains (and corresponding ontologies and taxonomies), to enumerate the categories for a given semantic domain, and to categorize a text or XML data blob.
2s1 5. An XML Web Service reference to another KBS frOlll Whlch the KBS gets its knowledge. In this mode, the KBS acts as a proxy. The KBS can be initialized to act as a proxy and to get i.ts supported semantic domains, ontologies, and taxonomies from another KBS.
S As explained above, the KIS (via the KDM) periodically sends XML objects to the KBS to categorize them for a given semantic domain.
4. ~ INFORMATIONAGENT (SEMANTIC $ROif~SER PLATFORM
n. Ove~~hiew The system client, in the preferred embodiment the Information Agent of the present invention, includes the semantic browses components and user interface that provide a semantic user experience. In the preferred embodiment, the Information Agent provides the following high-level services:
~ Allow users the power of context and time-sensitive semantic information retrieval via local and remote Information Agents.
~ Allow users to discover information on local and remote Agencies that are exposed via Agents thl-ough the XI~L Web Ser~rice of the present inventiono This 111fOm1at1~n 1S preferably classified lnt0 well-kll~Wn SeIllantlC CIaSSeS
SLICK aS
documents, email, email distribution lists, people, events, multimedia, and customers.
~ Allow users to browse a semantic view of infornzation found via Agents of the present invention.
~ Allow users to publish information to an Agency.
~ Allow users to dynamically link infornlation on their hard-drive, local network or a specific Agency with information found on Agents from another Agency. This facilitates dynamic e-linking and user-controlled browsing.
An advantage of the Infornation Agent of the present invention is that,users open up Agents similar how users open up documents from their file-system namespace.
The Information Agent will have its own environment that opens up semantic "worlds" of information. For example, ABC company may have an internal KIS Agency that has Agents for internal documents, email, etc. In addition, third-parties may host Agencies on the 2s2 Internet to hold iriforniation on industry reports, industry events, etc. In a preferred embodiment of the present invention, ABC company employees open Agents to discover information on the v~ternet that relates to their work as well as to semantically relate inforn~ation that is internal to ABC company to information that is external but relevant to ABC company.
b. ' Client Coygtrration In the preferred embodiment, the system client is able to semantically link information found locally as well as on remote Agencies. This is preferably accomplished through the use of an exposed Semantic Environment comprised of Agencies from a Global Agency Directory, Agencies on the local area network (published wia multicast or a peer-to-peer publishing system) and Agencies from a custom Agency Directory using Agent Discovery. The preferred client configuration is based on a framework having Agents and local Agencies, and includes a Semantic Enviromnent Manager, which manages locally saved Agents and Favorite Agents, essentially integrating the history and favorites metaphors, The Semantic Environment Manager uses Semantic ~uer~' Documents vrithin tlm Semantic Environment to present knowledge to users via the Semantic Environment Browser. The client configuration will also include the Agent Discovery information (e.g., Agency lists, Agency directory information, etc.).
c. Client Framework Specification Overview. The client framework specification provides floe service infrastructure for the Infornlation Agent user interface, and defines basic services and interfaces, includes core user interface components, and- provides an extensible, configurable environment for the main building blocks of the user interface of the Inforn~ation Agent. This section described the client framework specification according to a preferred embodiment of the present invention. The Framework Core defines base services, configuration, preferences and security mechanist's. The Core User Interface Components define the user interface services and modules that support server and Agent configuration, control and invocation, and some configuration for the Semantic Browser Framework. The Core User Interface COlIIpOIlelltS
are implemented as a Windows Shell extension and associated user interface (described below). The Semantic Browser Framework provides base query and results management services, and the framework for results presentation. The specifics of the user interface related to semantic object presentation are preferably configurable and extensible; even default presentation support is provided as a pre-installed "extension." The Semantic Browser Framework is preferably implemented as a set of behavior extensions to existing platforms used in Today's Web (e.g., Internet Explorer), and leverages the supported XML, XSLT, HTML/CSS and DOM functionality.
C~nt~~t. The ~ client framework builds upon semantic services c~iiiponents of the present invention including semantic quexy support, context and tlllle-SeI1s1t1Ve 52111ant1C
processing and linking of information, etc. 'The client framework is preferably built as a shell extension and platform (e.g., Internet Explorer) extensions, which provides functionality to users in the context of their e:~istin g tools and environment. For example, the Information Agent may be implemented as a Shell Extension (which extends the Windows Shell and.
employs the standard Explorer view and user interface models). In an alternative embodiment, the present invention is equally applicable in a standalone semantic browser application.
Requirements. The preferred requirements for the client framework relate to flexibility and extensibility. This ensures that the user interface can be easily and quickly adapted as there are more information object types, user profiles, etc.
Included are the following:
~ Provide support for Skins to manage the entire set of query results.
~ Allow for a wide range of approaches, include lists, tables, timed slides, etc.
~ Provide a screen-saver (or equivalent) mode.
~ Provide support for Skins that can be associated with an object class.

~ Ensure that there is a default Skin that can handle all classes.
~ Skins should be as simple as XSLT, but should allow script support, and possibly even code (with appropriate security restrictions).
~ Provide support fox browsing the Semantic Environment in the results view (to complement the Agent Tree View), including Agents (Smart, Dumb, and Special), Agencies, and Blenders.
Provide well-defined interfaces between components, and ensure that all communication must occur via t:he framework.
~ Provide a solid security model throughout the framework Framework 'ore Semantic Environment Manager (SEM). The SEM manages the creation, deletion, updating and browsing of Agents, Blenders, and Agencies on users' local machines. In addition, the SEM is responsible for listening to Agency multicast announcements, browsing Agencies on the enterprise directory (e.g., via LDAP), browsing Agencies on a custom directory, and browsing Agencies on the Global Agency Directory.
The SEM includes a storage layer that stores the metadata of every Agent on the system, including all the .Agent attributes (such as, the Agent name, desA~ription, cre,ati~rn time, last usage time, the Agent type (Smart, Dumb, Special, etc.), the information object type the Agent represents (for Agents created based on information type), the context type the Agent represents (for Special Agents or Agents created based on a Context Template), the attributes of the Agent, a reference to the XSLT or other script file that represents the Agent's Skin (including filter/sort preferences and other presentation schemes), the notification information and method (if requested for the Agent), and tlae buffer or file-pathlURL to the Agent's SQML query. The Information Agent (semantic browser) may store this Agent metadata in a local database, a store like the Windows registry, ox in an XML
file store on the local f le-system.
2s5 The SEM also uses the Agent attribute to indicate whether an Agent is a Favorite Agent. In addition, the SEM automatically deletes Agents that are not favorites and which are older than a configurable age limit (e.g., two weeks).
The Information Agent's Shell Extension and other components (such as the toolbar S and the Open Agent dialog) employ the SEM to provide Agent creation, deletion, browsing, updating, and management of Agents via its user interface.
Preferences Manager. This component manages all client-side preferences, providing services to persist the preferences, communicates with servers as needed to share preferences or support roaming, and supports, setting and obtaining preference values from other components.This component has associated user interface as well as some more specific preferences user interface components. The preferences are divided 1I1t0 sub-components, and may abstract the preferences for associated client classes. These include:
~ Core Preferences. This includes basic configuration such as user profile and 1 S persona inforgnati~n.
o Skin Preferences. This also associates preferred Skins with object classes, as well as the,preferred list Skin and screen saver Skins. There may be additional Skin-related preferences settings. .
This component also manages the set of locally available Skins. Downloadable Skins are preferably managed through this component.
Notification Manager. Notifications provide a means to indicate to users that there is new information available on a given Smart Agent. Users optionally configure a specific Smart Agent to support or provide notifications (it will be OFF by default for most Smart Agents), and will also configure how to present notifications to users. These notifications are presented by the Notification user interface component.

The Notification Manager is responsible for managing background, polling queries for the appropriate set of Smart Agents. The Live Information Manager is a parallel component that provides similar services to the Results Browser.
The Notification Manager gathers the list of Smart Agents marked for notification, and'~periodically polls the associated servers for new information. "New" is defined as "since the last poll [or query]." Each time the poll responds, it includes a timestamp indicator that the Notification Manager must persist, associated with the Agent.
The user interface associated with configuring the Notification Manager is preferably implemented in coordination with the Agent .Tree View. This enables notifications (e.g., a "Notify" popup menu option of each Smart Agent). The Notification Manager may also support alterlatives for notifying the user when there are new results available. Some options include a display style (e.g. bold, colored, etc.) for the Agent in the Agent Tree View, a reminder dialog, audio notification, or more exotic actions like email, IM
or SMS
notification.
I S client-Side Sec~a~-ity. Client-side security issues relate to extension code and Skins.
The Skins are preferably XSLT, but may also support script. In addition, the generated HTML may include references to ActiveX components and behaviors. The presentation sandbox may include security ' restrictions that prevent Skins from running potentially malicious code via script. For example, th.e implementation may completely disallow any unsigned code (including ActiveX and DHTML behaviors).
All client=server communication with Agencies are preferably hidden from the published interfaces (for Skins), which third parties will customize to provide custom Skins.
By isolating the functionality outside of the primary client runtime, the risk of security compromise can be reduced.
2s7 Core User httetface Contpottettts Agent Tree View. This is a Shell Extension Tree View that supports much of the core user interface for controlling and invoking Agents.
Semantic Environment Browsing User Interface. This provides user interface to allow users to browse the Semantic Environment. An example of this is the "Open Agent Dialog." This complements the Agent Tree View, which also displays a hierarchical view of the namespace (see screenshots).
Agent Inspector. This provides user interface to view the properties or edit (in the case of user-created Smart Agents) an individual Agent, Blender or Agency.
Browser Host. This is preferably a "wrapper" on the semantic browser core (e.g., the Internet Explorer browser runtime), which allows the presentation of a custom view of the Agents, Agencies, and Blenders in the Agent Tree View. It preferably does not have any user interface itself, but is a bridge component between the Shell Extension and the Browser Framework. ThIS C~IIIpOnellt is also preferably responsible for coordinating certain browser functionalit~r with the V~indo~~rs Shell usea° Interface, including in particular the na~rigation ("back/forward") mechanism, in order to provide a seamless "backlforward" user experience (wherein the user only has to deal with one "back/forward" history list).
Core Preferences UI. This provides a user interface for preferences related to Semantic Environment, server, persona and Agent management, as well as any other miscellaneous preference settings. This preferably includes primitive property sheet dialog, possibly divided up into separate sheets by i:unctional area. In the preferred embodiment, this should be a tabbed dialog user interface. . , Skin Preferences UI. This provides a user interface for preferences related to Skin management. This is preferably a property sheet dialog. The list of available Skins should be presented as a list, for selection. This user interface allows users to set the current Skins, as distinct from the default Skins. It preferably allows users to make the current Skin be the 2ss default. For per-Agent Skin preferences, this preferably allows users to select a Skin for the currently selected or opened Agent.
Notification UI. The user interface associated with configuring the Notification Manager is preferably implemented in coordination witlx the Agent Tree View.
The Notification Manager may also support alternatives for notifying users when there are new results available. Some options include a display style (e.g. bold, colored, etc.) for the Agent in the Agent Tree View, a reminder dialog, audio notification, or more exotic actions like email, lM or SMS notification. In the preferred embodiment, the user interface should include a tabbed dialog (or equivalent) to allow users to select out of the aforementioned notification schemes (and the like).
Screen saver. The user interfacE: preferably provides a special modality to the Results Browser that function like a screen saver, filling the screen in a theater-mode display.
In the preferred embodiment, special Skins. should be used. for.the screen-sfaver mode. These Skins could emphasize a dynamic display that can leverage a larger screen area, but could l~ also use larger fonts and more v~!idely paced layout.
Bnowser Fnanzew~rk Results Browser. The Results Browser is responsible for displaying the results of queries, and the information on any local rc;sources opened. The Results Browser preferably.
obtains one or more XML files from the Query Manager and merges these into a single XML ' file that represents a list of objects. The list itself may be filtered or sorted as an initial step.
The list as a structure is transformed by a special class of Skin (an XSLT
transform sheet, possibly including some script) that handles lists. The list-Skin creates the primary DHTML
(or the like) structure, e.g., a list, a table or perhaps a timed sequence.
Object Skins manage the individual DHTML items that present thc; information for each object instance. List-Skins may handle the dispatch of individual object Skins (mapping object class to Skin), but the Results Brower preferably provides default mappings of class to Skin for simplicity.

Useis may prefer a given form of presentation, and may choose default Skins (both for the list as well as for object classes). The original query (i.e. the SQML) may also include parameters that indicate which Skins should be used (especially which List-Skin). These will be passed to the Results Browser along with the results. The Results Brower uses the S facilities of the Skin Manager to select the right Skin to apply. Different rules may be employed for how user preferences and Agent (author) preferences are combined and prioritized.
When query results are composed of multiple distinct XML files, the Results Browser must merge these into a single XML document to provide a seamless user experience. The preferred embodiment provides for handling additional results dynamically.
This dynamic update mode .is preferably implemented by using a different template or perhaps a script method within the XSLT template. Alternatively, the list Skins may require a behavior (or local runtime component) to manage the logic of adding to the document with~ut disturbing user context.
l~ Querf l~~ab~ager (~r client-Site Semantic Query Pr~ces~a~r). The Query Manager is responsible for handling the communication with the server(s), executing the requests for information and gathering the XML result;. The resulting XML is passed to the Results Browses for presentation to users.
The Query Manager preferably provides the services to support the Smart Lens functionality. When a Smart Lens request is made, the results are returned as XML and are passed~to the Results Browses, preferably marked to indicate that they are Smart Lens results for a given object. Tlie Query Manager preferably includes the following sub-components .
that provide individual services to fulfill the query requests.
~ SQML Interpreter. This component must decompose passed SQML into a set of requests, possibly with linked resources. Each request or resource link resolves to a resource with an associated protocol (e.g. HTTP, or one of a number of local pseudo-protocols like outlook: or document:), and is dispatched to the associated protocol handler. A given SQML file may include a mix of network and local resource types.
Resource Handler Manager. This is,preferably a central registration mechanism for resource handlers. It is a minimal layer that associates protocols and pseudo protocols with handlers, and simplifies the dispatch of resource requests.
~ Resource Handlers. These are components that encapsulate the specifics of accessing the resources from a given "server." A resource handler does not resolve any linked resources. '.Chis is preferably the responsibility of the SQML
Interpreter (i.e. the SQML Interpreter will have already resolved linked resources and provided the associated meta data as part of the resource request to this handler). When the resource is a Semantic Web service, the component preferably bundles up the request and issuc;s it via http. When the resource is a local resource (e.g. a document: or Outlook: re;source), the resource handler handles the resource directly. For documents, the resource handler passes the document (a file:
IJRL) to the semantic meaning e~straction9 summarization, and categorization engine to extract meta-data. For email, the resource handler extracts messages from the exchange server, or local .PST files. Note that when there are links on a local resource, the local resource handler must perform the processing that filters results for semantic relatedness. This may be custom to the handler for efficienc~fy 2,p but a eentral, generic Relatedness Engine gill provide services for most cases.
o Relatedness Engine. This provides a place to gather the logic for comparing '. objects for relatedness. The comparison is preferably dependent on the,;
mix of schemas involved, but is otherwise a simple operation-given two objects, provide a measure of relatedness.
Filter/Sort Manager. The FilteriSon Manager supports the application of filters and sorts to the lists of results provided to the Results Browser. The Filter/Sort Manager leverages the services of the FilteriSort Preferences component to obtain user preferences for current settings. The main function of this component is to resolve general preferences, per-Agent preferences, and any settings defined in the actual results (this may or may not be supported). This component is notified by the Filter/Sort Preferences component when users change the currently applied filters and sorts. Because the associated user interface is part of a tool bar associated with the Shell Extension (i.e. its right-pane view), but the application of the functions happens in the Results Browser space, the control is typically indirect.
Lens Mode. When a Smart Lens is invoked, the Results Browser must generate Lens requests (queries) for objects that users choose. The queries are asynchronous so that users S can select Smart Lens queries for various objects and view the results as they are returned.
A suggested user interface for this is to reserve some real-estate for a Smart Lens icon. When in Smart Lens mode and the user clicks (or hovers) over the Smart Lens icon, a query is 4~
issued, and the icon changes to indicate that the query is' in progress. When results are returned, they are handled by the Results Browser and dedicated Smart Lens templates in the Skins, and the Smart Lens icon for an abject changes to indicate that xesults are available.
Clicking or hovering over the icon again will display the Smart Lens results in a Skin specific manner (see sample Smart Lens pane user interface). If the query is returned quickly enough, then the whole function preferably feels like; a popup activated by a hover or single click.
Deep Info View. If Deep Information is not available in the original results, this component generates the associated query. The query is preferably asynchronous. When .
results are returned to the Results Browser, they are processed through the appropriate Skin (using a special Deep Information template for each Skin), and the resulting HTML is incorporated into the results document under the associated object. The primary Skin for the schema inserts a Deep Information element in the HTML for the object so that the Results Browser knows where to incorporate the results. When Deep Information is available (whether as part of the original results or in response to a Deep Information query), the Skin either displays it directly or will indicate i:hat it is present, and some Skin-defined user interface will allow users to enable the display (e.g. as a popup window).
Context Info Manager. For object s currently displayed in the Results Browser, certain notifications are preferably provided by default. Two classes of new or additional info will be provided to users:

1. Additional results that were added to the server since the user made the original request. This is especially usc;ful for things such as headlines or active email threads. The results are handled by the Results Browser, by inserting the new objects into the view.
2. Context Templates and related information that would be of interest to the user.
This is generated by additional queries to a specific Agent (Smart Agent, Special Agent, Blender or Agency), using a particular object as 'context. The results axe handled similarly to the way that Deep Inforn~ation View and Smart Lens Mode results are handled, by processing the XML returned from the query, and inserting the resulting HTML into the existing HTML for the object. The Skin controls the display mechanisms and UI. An example of related information is "Breaking News" associated with the object.
Skin Manager. Maintain user preferences for list Skins, object Skins, and dependencies between list and object Skins (certain ~bject Skins may only make sense for a given list-Skin). The Skin Manager' also maintains parameters for each Skin that indicate constraints for the Skin, e.g. how much screen real-estate it requires, or modalities it best applies to. Considerable intelligence is preferably built in that assists the Results Browser to choose Skins for a range of screen and window si~,e constraints, as well as for modalities, accessibility, language and other constraints. Initial versions will likely be much simpler.
Skin Templates. This describes the structure of a Skin and how it is applied from within the Results Browser. A Skin is preferably XSLT templates that convert the~~results XML to XHTML (andlor other languages like SVG) or proprietary presentation platforn~s like Flash MX and ActionScript. The templates can also insert styling information, e.g. for CSS styling. The resulting presentation code (e.g., XHTML) can restrict the inclusion of code, for security reasons. Framework code in the Results Browser invokes the Skins. The , preferred embodiment includes the following classes of Skins:
~ List Skins (or layout Skins). A list Skin is used to transforni a list of objects returned from a query into some overall presentation structure. This may be a simple list, a table, or a timed sequence of slides. List Skins are not schema or object specific, although they may only support certain Skins, which can work within the constraints that the associated presentation form defines. E.g., a list Skin that defines a table layout may require, or prefer, object Skins that can produce information in a small rectangular format.
~ Object Skins. Object Skins are schema specific, and generate the presentation for an individual abject of a given information object type (or information class). It is possible to define a Skin for the; generic super-class (or any other super-class) that can serve as a default Skin for a range of derived classes or subclasses (presumably by omitting some details).
~ Context Skins. Context Skins are tied fo a particular Context Template, and ' ~ generate the presentation that v~ill most effectively convey the context indicated by the template.
~ Blender Skins. Blender Skins are designed to present the results from Blenders, These Skins should allow the user to view the results via the Agents contained in the Blender, via information obj ect type, or via a merged view that displays all tlae results as though they came from one source.
Skins preferably model constraints ;ouch as modality and presentation display area by handling the constraints (passed as parameters either statically or dynamically by events within the browser core itself). This is preferably supported by ii~iposing a restriction that list Skins must specify only acceptable object Skins. In an alternative approach, object Skins may be designed for a given list Skin, and the Results BrowserlSkin Manager chooses object Skins for the current list Skin.
List Skin Details. Users may choosf~ a single list Skin fox the current view and make it the default. List Skins may also be associated with individual Agents, in which case the generic default is overridden. The Results Browser invokes the list Skin to process the list of ~25 results, although the list Skin preferably does not actually Izandle the individual objects. It creates some per-object instance in the framework presentation (e.g., a timed entry in a sequence, or a cell in a table, or an item in a list), and then the object Skins will fill in the details.
Object Skin Details. The object Skins convert a particular schema to XHTML.
Support for asynchronous query results for things like Deep Information and Context Template inforn~ation are provided by invoking associated templates from the Results Browser (through the DOM) on the query results'XML, and then inserting the resulting XHTML into the, results document through DOM interfaces. There are preferably several individual templates within an object Skin, including: .
~ . Primary schema template. This is the main piece that generates XHTML, for default display. This must create: the wrappers for Deep Information, Smart Lens information, Context Template information content, and any script that provides user control over the associated display.
~ Deep Information template. This template handles the meta-infornzation for Deep Infornlation. It may be called for inline deep info provided with original results, or it may be called to handle asynchronously requested Deep Information.
Either way, it preferably generates XHTML in some form, which is inserted under the wrapper element for Deep Information. The insertion probably happens in .
XSLT for inline deep info, and is effected through DOM insertion for Deep Information query results.
~ Context information template. This template handles the results-information for context inforn~ation query results. It generates XHTML in some form, which is inserted under the ~~rrapper element for lure info. The insertion is effected tlarough DOM insertion for Deep Information query results.
o Smart Lens information templatte. This template handles the results-information for Smart Lens query results. It: generates XHTML in some form, which is inserted under the wrapper element for live info. The insertion is effected through DOM insertion for Deep Information query results.
In the preferred embodiment, the template cannot modify the other contents of the XHTML (even for the same object), so it will. be up to the Results Browser to coordinate the user interface changes that indicate when Deep Information, live inforniation or~Smart Lens results are available. The framework requires certain icons to be used (also for consistency), and for~.these to have regular names or element types, which will allow the Results Browser to find and ,modify them as needed. In addition, the Results Browser can create and raise events to indicate the state changes. The template-generated script can respond to these events, and display the associated information as desired.

Default Skins. In the preferred embodiment, a set of default Skins is provided. This preferably includes Skins for the basic object classes and a small set of list-Skins that allow a variety of views of query results. Preferable; list-Skins include:
~ A detailed list display (like the Windows Explorer details view) ~ A tabular Icon view (again, like the Windows Explorer Icon view, but somewhat richer) ~ A timed presentation view.
e. Client Framework In the, preferred embodiment, the sy:atem client includes Shell Extensions, a Presenter, and Skins used by the Presenter to display information with context and meaning.
Shell Extension. An Explorer Shell Extension is a Microsoft Windows software component that extends the ~Jind~ws Shell with custom code. Shell Extensions allow applications to use the Shell as a custom client, and also provide services such as clean integration with the desktop, the file-system, Internet Explorer, etc.
Examples of default shell extensions include "My Documents," "M~r Computer," "My Network Places,"
"P~ecycle Ein," and "Internet Explorer." ' The use of a Shell Extension in the preferred embodiment of the present invention has several advantages:
1. It provides a very clean way to provide a user; experience that seamlessly integrates with how knowledge-workers currently browse for information. In turn, this obviates the need to develop a proprietary client and allows for non-standard integration with Microsoft's Internet Explorer, "My Documents," etc.
2. It embraces Today's Web and provides a migration path for the transfer of content in Today's Web to the Information Nervous System of the present invention. For example, users preferable drag and drop documents from. their hard drive (via Microsoft Explorer) or from the Internet (via Internet Explorer) into remote Agents on the Shell Extension of the present invention. This is difficult and non-intuitive with a proprietary client. Nevertheless, the present invention contemplates portability to a proprietary client or to the equivalent of Shell Extension on non-Windows operating system and operating systems for non-personal computer devices.
The Shell Extensions of the present invention provide a view of users' Semantic Environment (e.g.; history, favorites and oi:her views). In the preferred embodiment, the Shell S Extension provides for the following:
1. Allows users i:o open an Agent, a document, a folder, or an address on the semantic browser's Semantic Environment. For an Agent, the client displays a custom "Open Agent" dialog box that allows users to browse the semantic browser's Semantic Environment. This preferably includes Agents in users' My Agents list, Agencies on the Global Agency Directory, Agencies on the local area network (announcing via multicast), sand Agencies on any custom Agency ;:~
Directory that users have confic;ured. [INSERT RELEVANT SCREEN SHOTS
ON UI] Opening an Agent .results in the client displaying the results of the query ~f that Agent. Opening a document opens the XML metadata for that document, consistent with the schema for the document object type. Opening a folder opens the XML metadata for a file-system folder.,Users are able to open the immediate or deep contents of the folder via the folder itself. Opening an address all~ws users to enter an~r address to be opened lair the client f~-ame~uorko This includes URLs (which open the XML metadata for the document), documents on the file-system, Agents, or objects (see "URL Naming Conventions" below}. In the case of Agents, the Agent URL is preferably entered as follows: Agent:l/<Agent name>@<Agency name>.<domain name>. This is analogous to the http://<URL>
naming convention for HTTP UF:Ls. The Agent:// prefix is required in this case because the Open Address option can open any address. In the case of the "Open Agent" option, users preferably do not need to add the prefix; the client framework automatically canonic:alizes the URL to include the prefix. This is similar to how users are able to enter "www.foo.com" into Today's browser without the qualifying http:// prefix.
It is anticipated that the client allows users the ability to open other objects, for example, Microsoft Outlook .PST files.
2. Allows users to browse, subscribe, and unsubscribe to or from Agents on a given Agency that supports User State.

3. Allows users to save invoked Agents or semantic query results into the My Agents list.
4. ~ Allows users to create Blenders, and to add and remove Agents to and from Blenders (including via drag and drop).
S. Notifies users when there are new Agencies on any of the Agency directories (for example, the Global Agency Directory, the Local Area Multicast Network or any custom Agency Directories) since the last time they checked 6. Notifies users when there are any new Agents on any particular Agency since the last time they checked 7. Provides drag and drop access to relational semantic queries for objects in the Semantic Environment. The Shell Extension allows users to drag and drop a document from the Semantic Environment (either on,a local drive, the network neigliborhood, the Intranet, or the Internet) to a shell folder representing an Agent.
This triggers a remote procedure call to the X1VIL VJeb Service for the given 1-S Agency with the document metadata as the argument.
8. Provides "paste" access to objects copied to the system clipboard. The present invention uses the system clipboard to allow users to copy any object for later access. In addition, the clipboard allows users to copy objects from other applications, for eacample, Microsoft Office application: (e.go~ email items from Outlook), from multimedia applications, and to copy data from any application.
9. Allows users to select an Agent as a Smart Lens. A Smart Lens allows users to view objects in the results view based on context from an Agent or any object that can be copied to the system clipboard. For example, ordinarily, if a document object is in the results view and users hover over the link representing the object, the object metadata is displayed. If, however, a Smart Lens is selected (for example by pasting it onto the results sheet), and users hover over the object, information that relates the object in the Smart Lens and the object underneath the cursor is displayed. For example, if users copy "People.Research.Al1" to the clipboard and paste it as a Smart :Lens, then hover over a document; metadata may be displayed in a balloon popup as follows: "Found 15 people in People.Research.All that are experts on this document." Other examples are "Found 3 people that might have written this document" and "Found 78 email messages relating to this object posted by people in People.Research.All".
Users decide whether to invoke any of the links in the metadata in the balloon popup. In an alternative embodiment, the popup may be displayed in a sidebar and does not DEMANDE OU BREVET VOLUMINEUX
LA PRESENTE PARTIE DE CETTE DEMANDE OU CE BREVET COMPREND
PLUS D'UN TOME.

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Claims (4)

1. A system for knowledge retrieval, management, delivery and presentation, comprising:
a server programmable to maintain semantic information;
a client providing a user interface for a user to communicate with the server;
and wherein the processor of the server operates to perform the steps of:
securing information from information sources;
semantic ascertaining one or more semantic properties of the information; and responding to user queries based upon one or more of the semantic properties.

2. The system of claim 1, wherein the first server further comprises structure or methodology directed to providing at least one of the following: a Semantic Network, a Semantic Data Gatherer, a Semantic Network Consistency Checker, an Inference Engine, a Semantic Query Processor, a Natural Language Parser, an Email Knowledge Agent, or a Knowledge Domain Manager.

3. The system of claim 1, wherein:
the information comprises objects or events; and the semantic properties of the objects or events are represented by active agents for semantically linking to the semantics and properties of the queries.

4. A method for knowledge retrieval, management, delivery and presentation for use with a server system programmed to add, maintain and host domain specific information that is used to classify and categorize semantic information, comprising:
securing information from information sources;
semantically linking the information from the information sources;
maintaining the semantic attributes of the semantically linked information;

delivering requested semantic information based upon user queries; and presenting semantic information according to customizable user preferences.