CN120873203A

CN120873203A - Knowledge graph-based enterprise information management method and system

Info

Publication number: CN120873203A
Application number: CN202510989960.3A
Authority: CN
Inventors: 杨秀财; 侯剑华; 张洋; 宋昊阳; 王媛媛
Original assignee: Sun Yat Sen University
Current assignee: Sun Yat Sen University
Priority date: 2025-07-17
Filing date: 2025-07-17
Publication date: 2025-10-31

Abstract

The invention relates to the technical field of enterprise information management, in particular to an enterprise information management method and system based on a knowledge graph; the method comprises the steps of collecting structured and unstructured information of departments, giving department identifications according to the structured and unstructured data, constructing a basic knowledge graph, inputting current task data, identifying task identifications, determining core departments, updating the knowledge graph according to the core departments, outputting task knowledge graph, dividing authority of each department according to the task knowledge graph, and outputting department information of the current task according to the authority of each department.

Description

Knowledge graph-based enterprise information management method and system

Technical Field

The invention relates to the technical field of enterprise information management, in particular to an enterprise information management method and system based on a knowledge graph.

Background

Existing enterprise task management systems rely heavily on database records, but unstructured data (e.g., documents, mail) account for over 80% and their information is underutilized. At present, when inquiring enterprise tasks, the traditional method is to manually associate departments and tasks and then mainly inquire related information of the department tasks and the like.

However, most of departments complete cooperatively in the process of completing the task, the information of each department aiming at the task is different, and the query authority among the departments is also different, so that the efficiency of querying the enterprise task and acquiring all relevant information is lower.

Disclosure of Invention

The invention aims to provide an enterprise information management method and system based on a knowledge graph, and aims to solve the technical problems that in the prior art, various departments have different inquiry authorities for different information aiming at tasks, so that the efficiency of inquiring enterprise tasks and acquiring all relevant information is low.

In order to achieve the above purpose, the enterprise information management method based on the knowledge graph adopted by the invention comprises the following steps:

collecting structured and unstructured information of departments, endowing the departments with identifiers according to the structured and unstructured data, and constructing a basic knowledge graph;

Inputting current task data, identifying a task identifier, determining a core department, updating a knowledge graph according to the core department, and outputting a task knowledge graph;

dividing each department authority according to the task knowledge graph, and outputting department information of the current task aiming at each department authority.

Wherein, in the steps of collecting the structured and unstructured information of the departments, giving the departments identification according to the structured and unstructured data, and constructing the basic knowledge graph:

collecting structured and unstructured information of each department, and extracting entities and relations of the structured and unstructured information;

Initializing an enterprise knowledge graph and outputting basic knowledge graph data.

Wherein, in the step of collecting the structured and unstructured information of each department:

for the structured information, associating the structured information with department ID, and extracting and integrating entities and relations in the structured information;

And marking department entities for unstructured information, and carrying out entity identification and relation extraction on the unstructured information.

Wherein, after the steps of labeling department entities for unstructured information and carrying out entity identification and relation extraction on the unstructured information:

structured and unstructured entities and relationships are fused.

Wherein, in the step of initializing the enterprise knowledge graph and outputting the basic knowledge graph data:

and according to the fused entities and relations, taking the entities as nodes and the relations as edges, and constructing a basic structure of the knowledge graph.

The method comprises the steps of inputting current task data, identifying a task identifier, determining a core department, updating a knowledge graph according to the core department, and outputting the task knowledge graph:

Inputting current task data, acquiring task description data, and extracting task identification;

and querying a plurality of departments associated with the task identifications in the basic knowledge graph, and screening core departments.

After the step of querying a plurality of departments associated with the task identifier in the basic knowledge graph and screening the core departments:

and dynamically adding task nodes and associated edges according to the core department, updating the basic knowledge graph, and outputting the task knowledge graph.

Wherein, in the step of dividing each department authority according to the task knowledge graph and outputting the department information of the current task for each department authority:

Predefining the corresponding relation between each department role and authority;

Distributing data access rights according to roles of each department in the task knowledge graph, and generating a rights list;

and outputting department information of the current task according to the authority of each department.

The method comprises the steps of distributing data access rights according to roles of various departments in a task knowledge graph and generating a rights list:

department permissions are derived layer by layer from task nodes.

The invention also provides an enterprise information management system based on the knowledge graph, which comprises a basic knowledge graph construction module, a task knowledge graph construction module and a department authority division module, wherein:

The basic knowledge graph construction module is used for collecting the structured and unstructured information of departments, endowing the departments with marks according to the structured and unstructured data and constructing a basic knowledge graph;

the task knowledge graph construction module is used for inputting current task data, identifying task identifications, determining core departments, updating knowledge graphs according to the core departments and outputting task knowledge graphs;

the department authority dividing module is used for dividing the authorities of all departments according to the task knowledge graph and outputting department information of the current task aiming at the authorities of all departments.

The enterprise information management method and system based on the knowledge graph adopt the basic knowledge graph construction module, the task knowledge graph construction module and the department authority division module to carry out the following steps of collecting department structured and unstructured information, giving department identifications according to the structured and unstructured data, constructing the basic knowledge graph, inputting current task data, identifying task identifications, determining core departments, updating the knowledge graph according to the core departments, outputting task knowledge graph, dividing each department authority according to the task knowledge graph, outputting department information of the current task according to each department authority, and improving the efficiency of inquiring enterprise tasks and acquiring all relevant information by the mode.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart illustrating steps of an enterprise information management method based on a knowledge graph according to the present invention.

Fig. 2 is a flow chart of the steps of S100 of the present invention.

Fig. 3 is a flowchart of the steps of S200 of the present invention.

Fig. 4 is a flowchart of the steps of S300 of the present invention.

Fig. 5 is a schematic structural diagram of the knowledge graph-based enterprise information management system of the present invention.

Fig. 6 is a schematic structural diagram of the electronic device of the present invention.

The system comprises a 401-basic knowledge graph construction module, a 402-task knowledge graph construction module and a 403-department authority division module.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. The term "if" as used herein may be interpreted as "at..once" or "when..once" or "in response to a determination", depending on the context.

Referring to fig. 1 to 4, the invention provides an enterprise information management method based on a knowledge graph, which comprises the following steps:

and S100, collecting structured and unstructured information of departments, endowing the departments with identifiers according to the structured and unstructured data, and constructing a basic knowledge graph.

In this embodiment, the structured and unstructured information of the departments is collected, and the departments are given identification according to the structured and unstructured data, and the basic knowledge graph is constructed. The specific process is as follows:

S101, collecting structured and unstructured information of each department, and extracting entities and relations of the structured and unstructured information;

s102, for the structured information, associating the structured information with department IDs, and extracting and integrating entities and relations in the structured information;

S103, marking department entities for unstructured information, and carrying out entity identification and relation extraction on the unstructured information;

S104, fusing structured and unstructured entities and relations;

s105, according to the fused entity and relation, taking the entity as a node and the relation as an edge, constructing a basic structure of the knowledge graph, initializing the enterprise knowledge graph, and outputting basic knowledge graph data.

In the above process, data is collected and entities and relationships are extracted, wherein:

Structured data collection, extracting department tables, personnel tables, project tables, etc. from enterprise databases (e.g., ERP, HR systems), such as:

SELECT department_id,department_name,project_id

FROM department_project_mapping;

Output examples:

department_id:DEPT-001

department _name research and development department

project_id:PROJ-A

Unstructured data collection, obtaining documents, mails, meeting offerings, etc. through crawlers or APIs, for example:

the text segment is that the research and development department is responsible for the technical scheme design of PROJ-A, and the market department assists in market research. "

Entity and relationship extraction:

structured data, directly extracting triples (e.g., in research and development department, in charge, at PROJ-A)).

Unstructured data-entities and relationships are identified using NLP models (e.g., BERT+ BiLSTM-CRF):

the entity is research and development department, market department, PROJ-A and technical scheme design.

Relationship, responsible and assistance.

Using department _id in the structured data as a unique identifier, associating entities and relations:

input (development department, in charge, at PROJ-A) + department _id=DEPT-001.

Output (DEPT-001, in charge, at PROJ-A).

Department entity labeling, labeling department names (e.g. "research and development department" →DEPT-001) using dictionary matching or model prediction.

The relation extraction formula:

Relation(e₁,e₂)＝Argmax_r∈RP(r∣e₁,e₂;θ)

where R is a set of predefined relationships (e.g., "responsible" for "collaboration") and θ is a model parameter.

Fusion structured and unstructured data if the same entity relationship in the structured and unstructured data is inconsistent (e.g. "research and development part cooperates with PROJ-A" in the structured data and "research and development part is responsible for PROJ-A" in the unstructured data), processing according to priority:

where α is a threshold, such as 0.8.

Constructing a basic knowledge graph, wherein:

nodes, departments, projects, resources, and other entities (e.g., DEPT-001, PROJ-A).

Edge-relationship type (e.g., responsible, collaborative).

Map storage-storing triples using a map database (e.g., neo4 j):

CREATE (d: device { id: 'DEPT-001', name: 'research and development part' })

CREATE (p: project { id: 'PROJ-A', name: 'A product development' })

CREATE (d) - [ (responsible)

S200, inputting current task data, identifying task identification, determining a core department, updating a knowledge graph according to the core department, and outputting a task knowledge graph.

In this embodiment, current task data is input, a task identifier is identified, a core department is determined, a knowledge graph is updated according to the core department, and a task knowledge graph is output. The specific process is as follows:

s201, inputting current task data, acquiring task description data and extracting task identification;

s202, inquiring a plurality of departments associated with task identifications in a basic knowledge graph, and screening core departments;

And S203, dynamically adding task nodes and associated edges according to the core department, updating the basic knowledge graph and outputting the task knowledge graph.

In the above process, extracting a task identifier, wherein:

and inputting task description, namely starting the development work of PROJ-B, leading a development part and cooperating a test part. "

Task identities and key departments (e.g. "research and development department", "test department") are extracted using TF-IDF.

The procedure for extraction using TF-IDF is as follows:

TF (word frequency) is calculated TF (TermFrequency). The frequency of occurrence of a word in the current document is measured. The formula:

example calculation (for document d):

the word "PROJ-B" is TF=1/8=0.125

The word "research and development department" tf=1/8=0.125

The word "collaboration" is tf=1/8=0.125

IDF (inverse document frequency) is calculated IDF (InverseDocumentFrequency). The differentiation of a word among all documents is measured. The formula:

Wherein, adding 1 smoothes out zero errors.

Suppose a set of documents:

document 1 "development of PROJ-A by the development department"

Document 2 (current document), "initiate development of PROJ-B, need development part leading"

Document 3 "test section responsible for cooperative test of PROJ-C"

Example calculation:

the word "PROJ-B":

the number of documents containing "PROJ-B" =1

IDF=log(3/2)≈0.176

The word "research and development department":

document number=2 including "development part

IDF=log(3/3)=0

And calculating TF-IDF weight, wherein the formula is as follows:

TF-IDF=TF×IDF

example results:

the words PROJ-B, TF 0.125, IDF 0.176, TF-IDF 0.022;

The words are research and development department, TF is 0.125, IDF is 0, TF-IDF is 0;

the words collaboration, TF 0.125, IDF 0.176, TF-IDF 0.022;

words start-up, TF 0.125, IDF 0.176, TF-IDF 0.022.

Task identification is extracted, and the highest TF-IDF word is directly taken, such as PROJ-B (weight is 0.022).

Words containing a prefix "PROJ-" such as "PROJ-B" are preferred.

If there are multiple high-weight words (e.g., "PROJ-B" and "collaboration"), a noun phrase or proper noun is selected.

Output result, task identification= "PROJ-B".

Querying departments associated with PROJ-B in the basic knowledge graph:

MATCH(d:Department)-[r]-(p:Project{id:'PROJ-B'})

RETURNd.name,TYPE(r)AS relation

Screening core departments the core departments are determined according to relation weights (such as 'responsible' > 'collaboration') or business rules (such as 'leading departments must be research and development classes').

Updating the knowledge graph, and dynamically adding task nodes and associated edges:

CREATE (t: task { id: 'TASK-20231001', name: 'PROJ-B research and development' })

MATCH (d: device { name: 'research and development' }), (p: project { id: 'PROJ-B' })

CREATE (d) - [ (responsible) - > (t), (t) - [ (associated) ] - > (p)

And outputting a task knowledge graph which comprises associated paths of tasks, departments and projects.

And S300, dividing each department authority according to the task knowledge graph, and outputting department information of the current task aiming at each department authority.

In this embodiment, the authority of each department is divided according to the task knowledge graph, and the department information of the current task is output for each department authority. The specific process is as follows:

s301, predefining corresponding relation between each department role and authority;

s302, deriving department rights from task nodes layer by layer according to roles of various departments in a task knowledge graph, distributing data access rights, and generating a rights list;

s303, outputting department information of the current task according to the authority of each department.

In the above procedure, role-rights mapping is predefined, rule example:

Roles are responsible, allowing operations are editing and deleting, and resource types are technical documents.

Roles, collaboration, permission operations, viewing and commenting, and resource types, test reports.

Deriving department rights, deriving the formula:

Wherein Roles (d) is a role set (such as 'responsible') of department d in the task, ops (r) is an operation set allowed by role r, and Resources (t, r) is a resource set associated with role r in task t.

Example deduction:

The research and development part role is 'responsible', allowing operation editing and deleting, and associating resource technology documents.

Rights { (edit, technical document), (delete, technical document) }.

The method comprises the steps of firstly collecting structured and unstructured information of departments, giving department identifications according to the structured and unstructured data, constructing a basic knowledge graph, then inputting current task data, identifying task identifications, determining core departments, updating the knowledge graph according to the core departments, outputting the task knowledge graph, dividing authority of each department according to the task knowledge graph, and outputting department information of the current task according to authority of each department.

The application also provides an embodiment of the enterprise information management system based on the knowledge graph, corresponding to the embodiment of the enterprise information management method based on the knowledge graph.

Fig. 5 is a block diagram of an enterprise information management system based on a knowledge graph, in accordance with an illustrative embodiment. Referring to fig. 5, the system may include a basic knowledge graph construction module 401, a task knowledge graph construction module 402, and a department rights partition module 403, wherein:

The basic knowledge graph construction module 401 is used for collecting structural and unstructured information of departments, giving department identification according to the structural and unstructured data, and constructing a basic knowledge graph;

the task knowledge graph construction module 402 is configured to input current task data, identify a task identifier, determine a core department, update a knowledge graph according to the core department, and output a task knowledge graph;

The department authority dividing module 403 is configured to divide each department authority according to the task knowledge graph, and output department information of the current task for each department authority.

In this embodiment, the basic knowledge graph construction module 401 collects the structured and unstructured information of departments, gives the departments an identifier according to the structured and unstructured data, and constructs a basic knowledge graph, the task knowledge graph construction module 402 inputs the current task data, identifies the task identifier, determines the core department, updates the knowledge graph according to the core department, and outputs the task knowledge graph, the department authority dividing module 403 divides each department authority according to the task knowledge graph, outputs the department information of the current task for each department authority, and by the above method, the efficiency of querying the enterprise task and acquiring all relevant information is improved.

The specific manner in which the various modules perform the operations in relation to the systems of the above embodiments have been described in detail in relation to the embodiments of the method and will not be described in detail herein.

For system embodiments, reference is made to the description of method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present application. Those of ordinary skill in the art will understand and implement the present application without undue burden.

Correspondingly, the application further provides electronic equipment, which comprises one or more processors, a memory and a storage, wherein the memory is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the enterprise information management method based on the knowledge graph. As shown in fig. 6, a hardware structure diagram of any device with data processing capability, where the enterprise information management system based on a knowledge graph is located, is provided in the embodiment of the present application, except for the processor, the memory and the network interface shown in fig. 6, where any device with data processing capability is located in the embodiment, generally, according to the actual function of the any device with data processing capability, other hardware may also be included, which will not be described herein again.

Correspondingly, the application further provides a computer readable storage medium, wherein computer instructions are stored on the computer readable storage medium, and the instructions are executed by a processor to realize the enterprise information management method based on the knowledge graph. The computer readable storage medium may be an internal storage unit, such as a hard disk or a memory, of any of the data processing enabled devices described in any of the previous embodiments. The computer readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), an SD card, a flash memory card (FLASH CARD), etc. provided on the device. Further, the computer readable storage medium may include both internal storage units and external storage devices of any device having data processing capabilities. The computer readable storage medium is used for storing the computer program and other programs and data required by the arbitrary data processing apparatus, and may also be used for temporarily storing data that has been output or is to be output.

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof.

Claims

1. A knowledge graph-based enterprise information management method, characterized by comprising the following steps:

Collect structured and unstructured information from departments, assign departmental identifiers based on the structured and unstructured data, and construct a basic knowledge graph;

Input the current task data, identify the task identifier, determine the core department, update the knowledge graph based on the core department, and output the task knowledge graph;

The permissions of each department are divided according to the task knowledge graph, and the department information of the current task is output according to the permissions of each department.

2. The knowledge graph-based enterprise information management method as described in claim 1, characterized in that, in the steps of collecting structured and unstructured departmental information, assigning departmental identifiers based on the structured and unstructured data, and constructing a basic knowledge graph:

Collect structured and unstructured information from various departments, and extract entities and relationships from the structured and unstructured information;

Initialize the enterprise knowledge graph and output basic knowledge graph data.

3. The knowledge graph-based enterprise information management method as described in claim 2, characterized in that, in the step of collecting structured and unstructured information from various departments:

For structured information, associate the structured information with department IDs, and extract and integrate the entities and relationships in the structured information;

For unstructured information, departmental entities are labeled, and entity recognition and relationship extraction are performed on the unstructured information.

4. The knowledge graph-based enterprise information management method as described in claim 3, characterized in that, after the steps of labeling departmental entities for unstructured information and performing entity recognition and relation extraction on the unstructured information:

It integrates structured and unstructured entities and relationships.

5. The enterprise information management method based on knowledge graph as described in claim 4, characterized in that, in the step of initializing the enterprise knowledge graph and outputting basic knowledge graph data:

Based on the merged entities and relationships, entities are used as nodes and relationships as edges to construct the basic structure of the knowledge graph.

6. The knowledge graph-based enterprise information management method as described in claim 1, characterized in that, in the steps of inputting current task data, identifying task identifiers, determining core departments, updating the knowledge graph based on the core departments, and outputting the task knowledge graph:

Input the current task data, obtain the task description data, and extract the task identifier;

Search the basic knowledge graph for multiple departments associated with the task identifier and filter for core departments.

7. The knowledge graph-based enterprise information management method as described in claim 6, characterized in that, after the step of querying multiple departments associated with the task identifier in the basic knowledge graph and filtering the core departments:

Based on the core departments, task nodes and associated edges are dynamically added to update the basic knowledge graph and output the task knowledge graph.

8. The enterprise information management method based on knowledge graph as described in claim 1, characterized in that, in the step of dividing the permissions of each department according to the task knowledge graph and outputting the department information of the current task for each department's permissions:

Predefine the correspondence between roles and permissions for each department;

Based on the roles of each department in the task knowledge graph, data access permissions are assigned and a permission list is generated;

Output the department information for the current task based on the permissions of each department.

9. The knowledge graph-based enterprise information management method as described in claim 8, characterized in that, in the step of allocating data access permissions and generating a permission list according to the roles of each department in the task knowledge graph:

Starting from the task nodes, departmental permissions are derived layer by layer.

10. A knowledge graph-based enterprise information management system, applied to the knowledge graph-based enterprise information management method as described in claim 1, characterized in that it includes a basic knowledge graph construction module, a task knowledge graph construction module, and a departmental permission division module; wherein:

The basic knowledge graph construction module is used to collect structured and unstructured information from departments, assign departmental identifiers based on the structured and unstructured data, and construct a basic knowledge graph.

The task knowledge graph construction module is used to input current task data, identify task identifiers, determine core departments, update the knowledge graph based on the core departments, and output the task knowledge graph.

The department permission division module is used to divide the permissions of each department according to the task knowledge graph, and output the department information of the current task for each department permission.