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WO2017048277A1 - Requête automatique - Google Patents

Requête automatique Download PDF

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Publication number
WO2017048277A1
WO2017048277A1 PCT/US2015/050937 US2015050937W WO2017048277A1 WO 2017048277 A1 WO2017048277 A1 WO 2017048277A1 US 2015050937 W US2015050937 W US 2015050937W WO 2017048277 A1 WO2017048277 A1 WO 2017048277A1
Authority
WO
WIPO (PCT)
Prior art keywords
disjunction
query
new term
new
term
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2015/050937
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English (en)
Inventor
George Forman
Olga SHAIN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hewlett Packard Enterprise Development LP
Original Assignee
Hewlett Packard Enterprise Development LP
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hewlett Packard Enterprise Development LP filed Critical Hewlett Packard Enterprise Development LP
Priority to PCT/US2015/050937 priority Critical patent/WO2017048277A1/fr
Publication of WO2017048277A1 publication Critical patent/WO2017048277A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/9032—Query formulation
    • G06F16/90324—Query formulation using system suggestions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities

Definitions

  • a variety of analytic tasks may be performed on data, and the results may be provided to a user.
  • the analytics tasks may include creating and running queries, clustering, pattern detection, classification, and others.
  • Figure 1 is a schematic illustration of an example system for automatically organizing selected terms from a text corpus into a query that includes a disjunction or a conjunction of a plurality of disjunctions in accordance with an implementation of the present disclosure.
  • Figure 2 illustrates a flowchart showing an example of a method for automatically organizing selected terms from a text corpus into a query that includes a disjunction or a conjunction of a plurality of disjunctions in accordance with an implementation of the present disclosure.
  • Figures 3 and 4 illustrate a flowchart showing an example of a method for automatically determining whether to place a new selected term into a new disjunction of the query or to add it to a specific existing disjunction in accordance with an implementation of the present disclosure.
  • Figure 5 is an example table including a plurality of input sequences of terms and the corresponding queries that are automatically generated based on the inputted terms in accordance with an implementation of the present disclosure.
  • Figure 6 is an example block diagram illustrating a computer- readable medium in accordance with an implementation of the present disclosure.
  • Many entities e.g., enterprises, organizations
  • databases for storage of data relating to the entities.
  • a business may maintain a database of customer information, and the customer information may be accessed by querying the database.
  • entities may generally store vast amounts of data originating from their business, including operations data, customer feedback data, financial data, Human Resource data, and so forth. Data stored in these databases may be accessed and updated for various purposes.
  • data stored in a database may be accessed and updated for various purposes. Quite often, a relatively large volume of data is searched for purposes of identifying and retrieving the closest matches to a search query.
  • the data may be searched for patterns or for various other purposes, such as classification, pattern detection, modeling and anomaly detection, as examples.
  • GUI Graphical User Interface
  • the most common GUI tool simply allows a user to type terms for the query and to connect the terms into the desired query.
  • Another GUI too! may show a collection of potentially interesting terms and mode selector buttons (e.g., "and,” "or”).
  • mode selector buttons e.g., "and," "or”
  • selecting multiple terms may create a conjunction among the terms.
  • the mode selector button if the user clicks the mode selector button and then it toggles to the OR" mode, the multiple selected terms may form a disjunction.
  • analytical decisions regarding the terms to be included in the query along with manually creating the query are required by the user.
  • term refers to an individual word, number, phrase, words with special symbols that may be used in a query.
  • disjunction refers to an OR” query among a set of terms and the term “conjunction” refers to an "AND” query among its sub-parts (e.g., a set of terms, set of disjunctions) or a single term/disjunction.
  • a disjunction of a single term is just the term itself (i.e., the records selected by that term).
  • the proposed techniques may identify a new term (e.g., selected by a user via a GUI) from a text corpus and may automatically determine whether to place the new term into a new disjunction of the query or to add it to a particular existing disjunction.
  • a processor may analyze a text corpus having terms and may automatically organize selected terms into a query including a disjunction or a conjunction of a plurality of disjunctions.
  • the processor may organize selected terms into a query by identifying a new term, automatically determine whether to place the new term into a new disjunction of the query or to add it to an existing disjunction of the query, and automatically determine into which existing disjunction to place the new term when the term is placed into an existing disjunction.
  • FIG. 1 is a schematic illustration of an example system 10 for automatically organizing selected terms from a text corpus into a query that includes a disjunction or a conjunction of a plurality of disjunctions.
  • the illustrated system 10 is capable of carrying out the techniques described below.
  • the system 10 is depicted as including at least one a computing device 100.
  • computing device 100 includes a processor 102, an interface 106, and a machine-readable storage medium 110.
  • processor 102 includes a processor 102, an interface 106, and a machine-readable storage medium 110.
  • FIG. 1 is a schematic illustration of an example system 10 for automatically organizing selected terms from a text corpus into a query that includes a disjunction or a conjunction of a plurality of disjunctions.
  • the illustrated system 10 is capable of carrying out the techniques described below.
  • the system 10 is depicted as including at least one a computing device 100.
  • computing device 100 includes a processor 102, an interface 106, and a machine-readable storage medium 110
  • the computing device 100 may communicate with a text corpus 150 and with an interactive user interface 160 (e.g., graphical user interface).
  • the text corpus 150 may include different types of data (i.e., plurality of terms) organized in documents, files, etc.
  • the data in the text corpus 150 may include text-like data, categorical data, numerical data, structured data, unstructured data, or any other type of data.
  • the computing device 100 may be any type of a computing device and may include at least engines 120-130. in one implementation, the computing device 100 may be an independent computing device. Engines 120-130 may or may not be part of the machine-readable storage medium 110. In another alternative example, engines 120-130 may be distributed between the computing device 100 and other computing devices.
  • the computing device 100 may include additional components and some of the components depicted therein may be removed and/or modified without departing from a scope of the system that allows for carrying out the functionality described herein.
  • Processor 102 may be central processing unit(s) (CPUs), microprocessor(s), and/or other hardware device(s) suitable for retrieval and execution of instructions (not shown) stored in machine-readable storage medium 110.
  • Processor 102 may fetch, decode, and execute instructions to identify different groups in a dataset.
  • processor 102 may include electronic circuits comprising a number of electronic components for performing the functionality of instructions.
  • Interface 106 may include a number of electronic components for communicating with various devices.
  • interface 106 may be an Ethernet interface, a Universal Serial Bus (USB) interface, an IEEE 1394 (Firewire) interface, an external Serial Advanced Technology Attachment (eSATA) interface, or any other physical connection interface suitable for communication with the computing device.
  • interface 106 may be a wireless interface, such as a wireless local area network (WLAN) interface or a near-field communication (NFC) interface that is used to connect with other devices/systems and/or to a network.
  • WLAN wireless local area network
  • NFC near-field communication
  • the user interface 160 and the computing device 100 may be connected via a network.
  • the network may be a mesh sensor network (not shown).
  • the network may include any suitable type or configuration of network to allow for communication between the computing device 100, the user interface 160, and any other devices/systems (e.g., other computing devices, displays), for example, to send and receive data to and from a corresponding interface of another device.
  • any other devices/systems e.g., other computing devices, displays
  • Each of the engines 120-130 may include, for example, a hardware device including electronic circuitry for implementing the functionality described below, such as control logic and/or memory.
  • the engines 120-130 may be implemented as any combination of hardware and software to implement the functionalities of the engines.
  • the hardware may be a processor and the software may be a series of instructions or microcode encoded on a machine-readable storage medium and executable by the processor. Therefore, as used herein, an engine may include program code (e.g., computer executable instructions), hardware, firmware, and/or logic, or combination thereof to perform particular actions, tasks, and functions described in more detail herein in reference to Figures 2-6.
  • the analysis engine 120 may analyze a text corpus (e.g., corpus 150) having a plurality of terms.
  • the text corpus may include terms organized in documents, files, etc.
  • Various techniques may be used to analyze the text corpus.
  • the query engine 130 may automatically organize selected terms into a query including a disjunction or a conjunction of a plurality of disjunctions. For example, the query engine 130 may identify a new term. Various techniques may be used to identify a new term - detecting user's input (e.g., typing a term, clicking on a term, highlighting a term), detecting an automatic selection of a term, etc. In addition, the query engine 130 may automatically determine whether to place the new term into a new disjunction of the query or to add it to an existing disjunction of the query. In other words, the query engine 130 evaluates the new term and determines the best placement for that new term.
  • the engine 130 may automaticaliy determine into which existing disjunction to place the new term when the new term is placed into an existing disjunction.
  • the query engine 130 may automaticaliy generate a query with a single disjunction that includes the new term when the query is empty. In other words, when the engine 130 determines that the query does not include any existing terms (i.e., is empty), the engine places the new term into a new disjunction that now forms the query.
  • Figure 2 illustrates a flowchart showing an example of a method 200 for automatically organizing selected terms from a text corpus into a query that includes a disjunction or a conjunction of a plurality of disjunctions.
  • execution of the method 200 is described below with reference to the system 10, the components for executing the method 200 may be spread among multiple devices/systems.
  • the method 200 may be implemented in the form of executable instructions stored on a machine-readable storage medium, and/or in the form of electronic circuitry.
  • the method 200 can be executed by at least one processor (e.g., processor 102 of device 100). In other examples, the method may be executed by another processor in communication with the system 10.
  • processor e.g., processor 102 of device 100
  • the method may be executed by another processor in communication with the system 10.
  • Various elements or blocks described herein with respect to the method 200 are capable of being executed simultaneously, in parallel, or in an order that differs from the illustrated serial manner of execution.
  • the method 200 is also capable of being executed using additional or fewer elements than are shown in the illustrated examples.
  • the method 200 begins at 210, where a processor may analyze a text corpus (e.g., corpus 150) having terms. Various techniques may be used to analyze the text corpus.
  • a text corpus e.g., corpus 150
  • Various techniques may be used to analyze the text corpus.
  • the processor may automaticaliy organize selected terms into a query that includes a disjunction or a conjunction of a plurality of disjunctions (i.e., a conjunctive normal form query). Therefore, the proposed method does not require extensive participation from a user but simply allows a user to select multiple terms together in any order to produce a query including a conjunction of a plurality of disjunctions.
  • Automatically organizing the selected terms into conjunctive normal form query may involve the following steps.
  • the processor may identify a new term.
  • the new term may be introduced by a user and the processor may detect the user's input (e.g., typing, clicking, highlighting). In another example, the new term may be introduced by the system.
  • the processor may automatically determine whether to place the new term into a new disjunction of the query or to add it to an existing disjunction of the query. In other words, the processor evaluates the new term and determines what the most accurate place in the query is for that new term.
  • the processor may automatically determine into which existing disjunction to place the new term when the new term is placed into an existing disjunction.
  • the proposed techniques may create a very specific and useful query based on the text corpus. The techniques used to determine whether to place the new term into a new disjunction of the query or to add it to a specific existing disjunction are described in additional details below in relation to Figures 3-5.
  • Figures 3 and 4 illustrate a flowchart showing an example of a method 300 automatically determining whether to place a new selected term into a new disjunction of the query or to add it to a specific existing disjunction.
  • the method shown in Figures 3-4 will be described in references to Figure 5, which shows an example table 500 including a plurality of input sequences 520 of terms and the corresponding queries 530 that are automatically generated based on the inputted terms.
  • the components for executing the method 300 may be spread among multiple devices/systems.
  • the method 300 may be implemented in the form of executable instructions stored on a machine-readable storage medium, and/or in the form of electronic circuitry.
  • the method 300 can be executed by at least one processor of a computing device (e.g., processor 102 of device 100).
  • the method 300 begins at 310, where a processor may determine whether the new term appears in the query or it does not appear in the corpus of terms. In other words, a processor may evaluate the incoming term in view of the existing query (if any). If the term appears in the query or it does not appear in the corpus of terms, the processor may ignore the new term (at 320). Thus, if the new term already appears in the query or if the term is not found in the corpus of terms, the processor may discard the term, rather than placing it into a query.
  • the processor may determine whether the existing evaluated query is empty or whether a query exists at all (at 330). If the processor determines that the query is empty or no query exists, the processor may automatically generate a query with a single disjunction that includes the new term (at 340). In one example, the processor may initially receive an initial conjunctive normal form query (which at first is an empty query), prior to identifying any selected terms to be included in the query. For example, as shown in row 540 of the table in Figure 5, when the new term is "line" and there are no other terms in the query, the return query includes one disjunction with the term "line" in it.
  • the processor determines that the there is an existing query that is not empty (e.g., the query includes at least one existing term)
  • the processor takes additional steps to evaluate the new term in relation to the query and the corpus of terms to determine where exactly to place the new term in the query.
  • the processor may build a proximity model for the new term based on the corpus, where the proximity model is based on terms in the corpus that are in proximity to the new term (at 350).
  • the goal of the proximity model is to help the processor to decide whether the new term is similar to one of the existing terms in the query (at this point, the processor knows that the query is not empty).
  • the processor may build a term count histogram of the terms in the corpus that appear near the new term (e.g., terms within a window of predetermined distance (within 2 terms, 3 terms, etc.), terms within the same sentence/paragraph/document, etc.).
  • the processor may build/train a model on the corpus to determine the usual terms that appear around the new term.
  • the proximity model may be reduced to a normalized form (e.g., by dividing the count for each term by the Euclidean vector length).
  • each proximity model can return a number for each proximal term and compare it with the corresponding number for the same term from another model.
  • the proximity model may be scaled (e.g. by Inverse Document Frequency or a similar technique).
  • the processor may determine a similarity score for each disjunction in the query, where the similarity score indicates the similarity of the new term to the disjunction ("each" may be just one if the query currently includes only a single disjunction).
  • Various techniques may be used to determine the similarity score for a single disjunction.
  • the processor may: a) compute a similarity measure of the proximity model of the new term and a proximity mode! of each term already included in the disjunction (i.e., the processor compares the similarity of their proximity models); b) identify the highest similarity measure computed across all terms of the disjunction; c) use the highest similarity measure as the similarity score for the disjunction.
  • the processor may compute the similarity of the new term with a proximity model for the entire disjunction. For example, the processor may build a proximity model for the entire disjunction by merging all the terms in the disjunction together (e.g., the processor may treat occurrences of "screen” and "display” as identical and may include terms in proximity to either one). Then, the processor may compute a similarity measure comparing the proximity model of the new term and the proximity model of the entire disjunction to determine the similarity score for the disjunction.
  • the processor determines the similarity score for each disjunction (e.g., based on the similarity between a term in the disjunction and the new term).
  • the processor may identify a disjunction having a maximum similarity to the new term.
  • the processor may select any disjunction that has the maximum similarity score as the disjunction being most similar to the new term (i.e. the disjunction having a maximum similarity to the new term).
  • the processor may compare the similarity score of the disjunction having the maximum similarity to the new term with a threshold (at 385).
  • the threshold may be preset, computed by the processor, set by a user, etc.
  • the processor may place the new term into a new disjunction when the similarity score of the disjunction having the maximum similarity to the new term is less than the threshold.
  • the processor may add the new term to the disjunction having the maximum similarity to the new term when the similarity score is greater than the threshold (at 395). That way, the processor automatically determines into which existing disjunction to place the new term when the new term is placed into an existing disjunction.
  • the query outputted by the processor using the proposed technique may be: (screen OR led OR display) AND (crack OR cracked OR damaged).
  • a term may be placed in an incorrect disjunction of the query and a user may prefer to correct that.
  • the processor may receive an indication that the disjunction location of a new term is improper (i.e., the term should not be included in that disjunction).
  • the indication received by the processor may be initiated by a user action (e.g., clicking on the term, highlighting the term).
  • a term and an improper disjunction location may be identified via a GUI (e.g., interface 160). For example, the user may click a single "fix-it" button which identifies the most recently placed term and its location as being improper.
  • the processor may automatically determine whether to place the new term into a new disjunction of the query or whether to add the new term to an existing disjunction while excluding the improper disjunction location of the new term.
  • the processor may repeat the techniques described of method 300 but may not include the specified improper disjunction in the new selection process. That correction process may be repeated multiple times (i.e., based on multiple user interactions) for a single new term and several disjunctions may be excluded from the selection process (e.g., until the user is satisfied with the disjunction placement of that term). For example, if the technique initially places a new term into a new disjunction and that disjunction is identified as an improper disjunction location, the processor may not place the new term into another new disjunction but may place it only into any of an existing disjunction.
  • a user may like to "disable” or “enable” an entire disjunction of the query from receiving a new term.
  • the processor may disable a disjunction of the query via the GUI and may enable a disjunction of the query via the GUI (e.g., based on an action by the user - clicking on the disjunction, highlighting the disjunction).
  • the GUI may provide a way to remove terms from the query (e.g., by deselecting a term by clicking a term that is currently selected, by clicking on a little X icon that appears next to term in the query). When this happens, the processor may remove the deselected term(s) from the query.
  • GUI may provide a way for the user to drag a term from place to place in the query (e.g., to fix a mistake or to put the term in a more intuitive order).
  • the user may also utilize the GUI to drag a term to a holding area for future use.
  • Figure 6 illustrates a computer 601 and a non-transitory machine- readable medium 605 according to an example.
  • the computer 601 maybe similar to the computing device 100 of the system 10 or may include a plurality of computers.
  • the computer may be a server computer, a workstation computer, a desktop computer, a laptop, a mobile device, or the like, and may be part of a distributed system.
  • the computer may include one or more processors and one or more machine-readable storage media.
  • the computer may include a user interface (e.g., touch interface, mouse, keyboard, or gesture input device).
  • Computer 601 may perform methods 200-300 and variations thereof. Additionally, the functionality implemented by computer 601 may be part of a larger software platform, system, application, or the like. Computer 601 may be connected to a database (not shown) via a network.
  • the network may be any type of communications network, including, but not limited to, wire-based networks (e.g., cable), wireless networks (e.g., cellular, satellite), cellular telecommunications network(s), and IP-based telecommunications network(s) (e.g., Voice over Internet Protocol networks).
  • the network may also include traditional land!ine or a public switched telephone network (PSTN), or combinations of the foregoing.
  • PSTN public switched telephone network
  • the computer 601 may include a processor 603 and non-transitory machine-readable storage medium 605.
  • the processor 603 e.g., a central processing unit, a group of distributed processors, a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a graphics processor, a multiprocessor, a virtual processor, a cloud processing system, or another suitable controller or programmable device
  • ASIC application-specific integrated circuit
  • the storage medium 605 may include any suitable type, number, and configuration of volatile or non- volatile machine-readable storage media to store instructions and data.
  • machine-readable storage media in include read-only memory (“ROM”), random access memory (“RAM”) (e.g., dynamic RAM ["DRAM”], synchronous DRAM ["SDRAM”]), electrically erasable programmable read-only memory (“EEPROM”), magnetoresistive random access memory (MRAM), memristor, flash memory, SD card, floppy disk, compact disc read only memory (CD-ROM), digital video disc read only memory (DVD-ROM), and other suitable magnetic, optical, physical, or electronic memory on which software may be stored.
  • ROM read-only memory
  • RAM random access memory
  • EEPROM electrically erasable programmable read-only memory
  • MRAM magnetoresistive random access memory
  • CD-ROM compact disc read only memory
  • DVD-ROM digital video disc read only memory
  • Software stored on the non-transitory machine-readable storage media 605 and executed by the processor 603 includes, for example, firmware, applications, program data, filters, rules, program modules, and other executable instructions.
  • the processor 603 retrieves from the machine-readable storage media 605 and executes, among other things, instructions related to the control processes and methods described herein.
  • the processor 603 may fetch, decode, and execute instructions 607- 611 among others, to implement various processing.
  • processor 603 may include at least one integrated circuit (IC), other control logic, other electronic circuits, or combinations thereof that include a number of electronic components for performing the functionality of instructions 607-611. Accordingly, processor 603 may be implemented across multiple processing units and instructions 607-611 may be implemented by different processing units in different areas of computer 601.
  • IC integrated circuit
  • the instructions 607-611 when executed by processor 603 can cause processor 603 to perform processes, for example, methods 200-300, and/or variations and portions thereof. In other examples, the execution of these and other methods may be distributed between the processor 603 and other processors in communication with the processor 603.
  • analysis instructions 607 may cause processor 603 to analyze a text corpus having terms. These instructions may function similarly to the techniques described in block 210 of method 200.
  • Query instructions 611 may cause the processor 603 to automatically organize selected terms into a query including a disjunction or a conjunction of a plurality of disjunctions. These instructions may function similarly to the techniques described block 220 of method 200. Further, query instructions 611 may cause the processor 603 to identify a new term (e.g., based on user's input as explained above), ignore the new term if the term appears in the query or it does not appear in the corpus of terms, automatically determine whether to place the new term into a new disjunction of the query or to add it to an existing disjunction of the query, and automatically determine into which existing disjunction to place the new term when the new term is placed into an existing disjunction.
  • a new term e.g., based on user's input as explained above
  • Query instructions 611 may also cause the processor 603 to: build a proximity model for the new term based on the corpus, where the proximity model is based on terms in the corpus that are in proximity to the new term; determine a similarity score for each disjunction in the query, the similarity score indicating the similarity of the new term to the disjunction; and identify the disjunction having a maximum similarity to the new term.
  • Query instructions may further cause the processor 603 to: compare the similarity score of the disjunction having the maximum similarity with a threshold; place the new term into a new disjunction when the similarity score of the disjunction having the maximum similarity to the new term is less than the threshold; and add the new term to the disjunction having the maximum similarity to the new term when the similarity score is greater than the threshold.
  • These instructions may function similarly to the techniques described in blocks 230-250 of method 200 and method 300.

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  • Artificial Intelligence (AREA)
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  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention concerne, dans un mode de réalisation, un procédé décrit à titre d'exemple. Le procédé comporte l'étape consistant à faire analyser, par un processeur, un corpus de texte comprenant des termes. Le procédé comporte également les étapes consistant à faire automatiquement organiser, par le processeur, des termes sélectionnés en une requête comprenant une disjonction ou une conjonction d'une pluralité de disjonctions: en identifiant un nouveau terme, en déterminant automatiquement s'il convient de placer le nouveau terme dans une nouvelle disjonction de la requête ou de l'ajouter à une disjonction existante de la requête, et en déterminant automatiquement dans quelle disjonction existante placer le nouveau terme lorsque le nouveau terme est placé dans une disjonction existante.
PCT/US2015/050937 2015-09-18 2015-09-18 Requête automatique Ceased WO2017048277A1 (fr)

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Citations (5)

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Publication number Priority date Publication date Assignee Title
US6405190B1 (en) * 1999-03-16 2002-06-11 Oracle Corporation Free format query processing in an information search and retrieval system
US20090150354A1 (en) * 2007-12-07 2009-06-11 Aisin Aw Co., Ltd. Search devices, methods, and programs for use with navigation devices, methods, and programs
US20090319498A1 (en) * 2008-06-24 2009-12-24 Microsoft Corporation Query processing pipelines with single-item and multiple-item query operators
KR20110020117A (ko) * 2009-08-21 2011-03-02 (주)윕스 검색식 작성 방법 및 장치
US20110066620A1 (en) * 2009-09-11 2011-03-17 IntelljResponse Systems Inc. Automated Boolean Expression Generation for Computerized Search and Indexing

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6405190B1 (en) * 1999-03-16 2002-06-11 Oracle Corporation Free format query processing in an information search and retrieval system
US20090150354A1 (en) * 2007-12-07 2009-06-11 Aisin Aw Co., Ltd. Search devices, methods, and programs for use with navigation devices, methods, and programs
US20090319498A1 (en) * 2008-06-24 2009-12-24 Microsoft Corporation Query processing pipelines with single-item and multiple-item query operators
KR20110020117A (ko) * 2009-08-21 2011-03-02 (주)윕스 검색식 작성 방법 및 장치
US20110066620A1 (en) * 2009-09-11 2011-03-17 IntelljResponse Systems Inc. Automated Boolean Expression Generation for Computerized Search and Indexing

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