Enterprise information management method and system based on knowledge graph

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 following steps: collecting department structured and unstructured information, endowing a department identifier according to 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 the 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; the system comprises a basic knowledge graph construction module, a task knowledge graph construction module and a department authority division module. By means of the mode, the efficiency of querying the enterprise tasks and obtaining all related information is improved.

Description

Technical Field

[0001] This invention relates to the field of enterprise information management technology, and in particular to an enterprise information management method and system based on knowledge graphs. Background Technology

[0002] Existing enterprise task management systems largely rely on database records, but unstructured data (such as documents and emails) accounts for over 80%, and its information is not being fully utilized. Currently, when querying enterprise tasks, the traditional method involves manually linking departments and tasks, and then primarily retrieving relevant information about tasks within that department.

[0003] However, most tasks are completed through collaboration among multiple departments. Each department has different information about the task and different query permissions, resulting in low efficiency in querying enterprise tasks and obtaining all relevant information. Summary of the Invention

[0004] The purpose of this invention is to provide a knowledge graph-based enterprise information management method and system, which aims to solve the technical problem in the prior art where different departments have different information for the task and different query permissions between departments, resulting in low efficiency in querying enterprise tasks and obtaining all relevant information.

[0005] To achieve the above objectives, the present invention employs a knowledge graph-based enterprise information management method, comprising the following steps:

[0006] Collect structured and unstructured information from departments, assign departmental identifiers based on the structured and unstructured data, and construct a basic knowledge graph;

[0007] 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;

[0008] 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.

[0009] Among the steps are: collecting structured and unstructured information from departments, assigning departmental identifiers based on the structured and unstructured data, and constructing a basic knowledge graph;

[0010] Collect structured and unstructured information from various departments, and extract entities and relationships from the structured and unstructured information;

[0011] Initialize the enterprise knowledge graph and output basic knowledge graph data.

[0012] Among the steps involved in collecting structured and unstructured information from various departments:

[0013] For structured information, associate the structured information with department IDs, and extract and integrate the entities and relationships in the structured information;

[0014] For unstructured information, departmental entities are labeled, and entity recognition and relationship extraction are performed on the unstructured information.

[0015] After the steps of labeling departmental entities for unstructured information and performing entity recognition and relation extraction on the unstructured information:

[0016] It integrates structured and unstructured entities and relationships.

[0017] In the steps of initializing the enterprise knowledge graph and outputting basic knowledge graph data:

[0018] 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.

[0019] Among the steps, the following steps are involved: 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:

[0020] Input the current task data, obtain the task description data, and extract the task identifier;

[0021] Search the basic knowledge graph for multiple departments associated with the task identifier and filter for core departments.

[0022] After the steps of querying multiple departments associated with the task identifier in the basic knowledge graph and filtering for core departments:

[0023] 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.

[0024] In the step of dividing departmental permissions based on the task knowledge graph and outputting the departmental information for the current task according to the departmental permissions:

[0025] Predefine the correspondence between roles and permissions for each department;

[0026] Based on the roles of each department in the task knowledge graph, data access permissions are assigned and a permission list is generated;

[0027] Output the department information for the current task based on the permissions of each department.

[0028] Among them, the steps of assigning data access permissions and generating a permission list based on the roles of each department in the task knowledge graph are as follows:

[0029] Starting from the task nodes, departmental permissions are derived layer by layer.

[0030] This invention also provides a knowledge graph-based enterprise information management system, including a basic knowledge graph construction module, a task knowledge graph construction module, and a departmental permission division module; wherein:

[0031] 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.

[0032] 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.

[0033] 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.

[0034] This invention discloses a knowledge graph-based enterprise information management method and system, which employs a basic knowledge graph construction module, a task knowledge graph construction module, and a department permission division module to perform the following steps: collecting structured and unstructured department information, assigning department identifiers based on the structured and unstructured data, and constructing a basic knowledge graph; 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; dividing the permissions of each department based on the task knowledge graph, and outputting the department information of the current task for each department's permissions; through the above methods, the efficiency of querying enterprise tasks and obtaining all relevant information is improved. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the steps of the knowledge graph-based enterprise information management method of the present invention.

[0037] Figure 2 This is a flowchart of steps S100 of the present invention.

[0038] Figure 3 This is a flowchart of steps S200 of the present invention.

[0039] Figure 4 This is a flowchart of steps S300 of the present invention.

[0040] Figure 5 This is a schematic diagram of the structural principle of the knowledge graph-based enterprise information management system of the present invention.

[0041] Figure 6 This is a schematic diagram of the electronic device of the present invention.

[0042] 401 - Basic Knowledge Graph Construction Module, 402 - Task Knowledge Graph Construction Module, 403 - Departmental Permission Division Module. Detailed Implementation

[0043] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0044] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0045] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0046] Please see Figures 1-4 This invention provides a knowledge graph-based enterprise information management method, comprising the following steps:

[0047] S100: Collect structured and unstructured information from departments, assign departmental identifiers based on the structured and unstructured data, and construct a basic knowledge graph.

[0048] In this implementation, structured and unstructured information about departments is collected, departmental identifiers are assigned based on the structured and unstructured data, and a basic knowledge graph is constructed. The specific process is as follows:

[0049] S101: Collect structured and unstructured information from various departments, and extract entities and relationships from the structured and unstructured information;

[0050] S102: For structured information, associate the structured information with the department ID, and extract and integrate the entities and relationships in the structured information;

[0051] S103: For unstructured information, label departmental entities and perform entity recognition and relation extraction on the unstructured information;

[0052] S104: Integrate structured and unstructured entities and relationships;

[0053] S105: Based on the merged entities and relationships, construct the basic structure of the knowledge graph by using entities as nodes and relationships as edges, initialize the enterprise knowledge graph, and output the basic knowledge graph data.

[0054] In the above process, data is collected and entities and relationships are extracted, including:

[0055] Structured data acquisition: Extracting department tables, personnel tables, project tables, etc. from enterprise databases (such as ERP, HR systems), for example:

[0056] SELECT department_id,department_name,project_id

[0057] FROM department_project_mapping;

[0058] Output example:

[0059] department_id: DEPT-001

[0060] department_name: Research and Development Department

[0061] project_id: PROJ-A

[0062] Unstructured data acquisition: Obtaining documents, emails, meeting minutes, etc., through web crawlers or APIs, for example:

[0063] Text excerpt: "The R&D department is responsible for the technical solution design of PROJ-A, and the marketing department assists in market research."

[0064] Entity and Relation Extraction:

[0065] Structured data: Directly extract triples (e.g., (R&D Department, in charge, in PROJ-A)).

[0066] Unstructured data: Identifying entities and relationships using NLP models (such as BERT+BiLSTM-CRF):

[0067] Entities: R&D Department, Marketing Department, PROJ-A, Technical Solution Design.

[0068] Relationship: responsible for, assisting.

[0069] Use the department_id from the structured data as a unique identifier to associate entities and relationships:

[0070] Input: (R&D Department, in charge, in PROJ-A) + department_id = DEPT-001.

[0071] Output: (DEPT-001, in charge, in PROJ-A).

[0072] Department entity labeling: Use dictionary matching or model prediction to label department names (e.g., "R&D Department" → DEPT-001).

[0073] Relation extraction formula:

[0074] Relation(e1,e2)=Argmax r∈R P(r∣e1,e2;θ)

[0075] Where R is a predefined set of relations (such as "responsible" and "collaborate"), and θ is the model parameter.

[0076] When merging structured and unstructured data, if the relationships of the same entity are inconsistent in the structured and unstructured data (e.g., "R&D Department Collaborates on PROJ-A" in structured data, and "R&D Department is Responsible for PROJ-A" in unstructured data), then they should be processed according to priority:

[0077]

[0078] Where α is the threshold, such as 0.8.

[0079] Construct a basic knowledge graph, in which:

[0080] Nodes: Entities such as departments, projects, and resources (e.g., DEPT-001, PROJ-A).

[0081] Edge: Relationship type (e.g., responsible, collaborative).

[0082] Graph storage: Use a graph database (such as Neo4j) to store triples:

[0083] CREATE(d:Department{id:'DEPT-001',name:'R&D Department'})

[0084] CREATE(p:Project{id:'PROJ-A',name:'Product Development A'})

[0085] CREATE(d)-[:Responsible for]->(p)

[0086] S200: Input current task data, identify task identifiers, determine core departments, update the knowledge graph based on core departments, and output task knowledge graph.

[0087] In this implementation, current task data is input, task identifiers are identified, core departments are determined, the knowledge graph is updated based on the core departments, and the task knowledge graph is output. The specific process is as follows:

[0088] S201: Input the current task data, obtain the task description data, and extract the task identifier;

[0089] S202: Query multiple departments associated with the task identifier in the basic knowledge graph and filter the core departments;

[0090] S203: Based on the core departments, dynamically add task nodes and related edges, update the basic knowledge graph, and output the task knowledge graph.

[0091] In the above process, the task identifier is extracted, where:

[0092] Input task description: "Initiate the development of PROJ-B, which requires the R&D department to take the lead and the testing department to cooperate."

[0093] Use TF-IDF to extract task identifiers and key departments (such as "R&D Department" and "Testing Department").

[0094] The process of extracting using TF-IDF is as follows:

[0095] Calculate TF (Term Frequency): TF measures the frequency of a word in the current document. Formula:

[0096]

[0097] Example calculation (for document d):

[0098] The term "PROJ-B" has a TF of 1 / 8 = 0.125.

[0099] The term "Research and Development Department" has a TF of 1 / 8 = 0.125.

[0100] The word "collaboration": TF = 1 / 8 = 0.125

[0101] Calculate IDF (Inverse Document Frequency). IDF measures the distinctiveness of a word across all documents. Formula:

[0102]

[0103] Adding 1 smooths out division by zero errors.

[0104] Assume a collection of documents:

[0105] Document 1: "PROJ-A development was completed by the R&D department."

[0106] Document 2 (current document): "Initiating PROJ-B development requires leadership from the R&D department."

[0107] Document 3: "The Testing Department is responsible for collaborative testing of PROJ-C".

[0108] Example calculation:

[0109] The term "PROJ-B":

[0110] Number of documents containing "PROJ-B" = 1

[0111] IDF = log(3 / 2) ≈ 0.176

[0112] The term "Research and Development Department":

[0113] Number of documents containing "R&D Department" = 2

[0114] IDF = log(3 / 3) = 0

[0115] The formula for calculating TF-IDF weights is:

[0116] TF-IDF = TF × IDF

[0117] Example results:

[0118] Keywords: PROJ-B; TF: 0.125; IDF: 0.176; TF-IDF: 0.022;

[0119] Keywords: R&D department; TF: 0.125; IDF: 0; TF-IDF: 0;

[0120] Keywords: collaboration; TF: 0.125; IDF: 0.176; TF-IDF: 0.022;

[0121] Keywords: Startup; TF: 0.125; IDF: 0.176; TF-IDF: 0.022.

[0122] Extract the task identifier by directly taking the word with the highest TF-IDF value, such as "PROJ-B" (weight 0.022).

[0123] Prioritize words that contain the prefix "PROJ-" (such as "PROJ-B").

[0124] If multiple high-weight words exist (such as "PROJ-B" and "collaboration"), choose a noun phrase or proper noun.

[0125] Output: Task ID = "PROJ-B".

[0126] Search for departments associated with PROJ-B in the basic knowledge graph:

[0127] MATCH(d:Department)-[r]-(p:Project{id:'PROJ-B'})

[0128] RETURNd.name,TYPE(r)AS relation

[0129] Screening core departments: Determine core departments based on relationship weight (e.g., "responsible" > "collaborating") or business rules (e.g., "the leading department must be R&D-related").

[0130] Update the knowledge graph by dynamically adding task nodes and associated edges:

[0131] CREATE(t:Task{id:'TASK-20231001',name:'PROJ-B R&D'})

[0132] MATCH(d:Department{name:'R&D Department'}),(p:Project{id:'PROJ-B'})

[0133] CREATE(d)-[:responsible]->(t),(t)-[:related]->(p)

[0134] Output a task knowledge graph: containing the association paths of tasks, departments, and projects.

[0135] S300: Divide the permissions of each department according to the task knowledge graph, and output the department information of the current task according to the permissions of each department.

[0136] In this implementation, departmental permissions are defined based on the task knowledge graph, and departmental information for the current task is output for each department's permissions. The specific process is as follows:

[0137] S301: Predefine the correspondence between roles and permissions for each department;

[0138] S302: Based on the roles of each department in the task knowledge graph, starting from the task nodes, deduce department permissions layer by layer, allocate data access permissions, and generate a permission list;

[0139] S303: Outputs department information for the current task based on department permissions.

[0140] In the above process, a predefined role-permission mapping rule example is provided:

[0141] Role: Responsible for; Permitted actions: Edit, Delete; Resource type: Technical document.

[0142] Role: Collaborator; Allowed Actions: View, Comment; Resource Type: Test Report.

[0143] Derivation of departmental authority, formula:

[0144]

[0145] Where: Roles(d) is the set of roles of department d in the task (e.g., "responsible for"); Ops(r) is the set of operations allowed for role r; Resources(t,r) is the set of resources in task t associated with role r.

[0146] Example derivation:

[0147] The R&D department's role is "responsible," and they are allowed to edit, delete, and associate resources and technical documents.

[0148] Permissions: {(Edit, Technical Document), (Delete, Technical Document)}.

[0149] Output the department information for the current task based on the permissions of each department.

[0150] In this invention, firstly, structured and unstructured information of departments is collected, departmental identifiers are assigned based on the structured and unstructured data, and a basic knowledge graph is constructed; then, current task data is input, task identifiers are identified, core departments are determined, the knowledge graph is updated based on the core departments, and the task knowledge graph is output; finally, 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; through the above method, the efficiency of querying enterprise tasks and obtaining all relevant information is improved.

[0151] Corresponding to the aforementioned embodiments of the knowledge graph-based enterprise information management method, this application also provides embodiments of a knowledge graph-based enterprise information management system.

[0152] Figure 5 This is a block diagram of a knowledge graph-based enterprise information management system according to an exemplary embodiment. (Refer to...) Figure 5 The system may include: a basic knowledge graph construction module 401, a task knowledge graph construction module 402, and a departmental permission division module 403; wherein:

[0153] The basic knowledge graph construction module 401 is used to collect structured and unstructured information of departments, assign departmental identifiers based on the structured and unstructured data, and construct a basic knowledge graph.

[0154] The task knowledge graph construction module 402 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.

[0155] The department permission division module 403 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.

[0156] In this embodiment, the basic knowledge graph construction module 401 collects structured and unstructured information of departments, assigns departmental identifiers based on the structured and unstructured data, and constructs a basic knowledge graph; the task knowledge graph construction module 402 inputs current task data, identifies task identifiers, determines core departments, updates the knowledge graph based on the core departments, and outputs the task knowledge graph; the departmental permission division module 403 divides the permissions of each department based on the task knowledge graph, and outputs the departmental information of the current task for each departmental permission; through the above methods, the efficiency of querying enterprise tasks and obtaining all relevant information is improved.

[0157] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0158] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0159] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the knowledge graph-based enterprise information management method described above. Figure 6 The diagram shown is a hardware structure diagram of any data processing-capable device used in an embodiment of the knowledge graph-based enterprise information management system provided by the present invention. (Except for...) Figure 6In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0160] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the knowledge graph-based enterprise information management method described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0161] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0162] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A knowledge graph-based enterprise information management method, characterized in that, Includes 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 information from departments, 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 process 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 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 knowledge graph-based enterprise information management method as described in claim 4, characterized in that, In the steps 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 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 querying multiple departments associated with the task identifier in the basic knowledge graph and filtering for 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 knowledge graph-based enterprise information management method as described in claim 1, characterized in that, In the steps of dividing departmental permissions based on the task knowledge graph and outputting the departmental information for the current task based on the departmental 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 steps of assigning data access permissions and generating a permission list based on 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; among which: 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.

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