Enterprise knowledge management method, system, electronic device and storage medium

Abstract

The present invention proposes an enterprise knowledge management method, system, electronic device and storage medium. The method and technical solution include obtaining structured data in enterprise knowledge, and making a first definition for the content to be graphed in the structured data, and generating a knowledge graph for the content to be graphed in the structured data according to the first definition; obtaining unstructured data in the enterprise knowledge, and making a second definition for the content to be graphed in the unstructured data, and adding the content to be graphed in the unstructured data into the knowledge graph according to the second definition; setting the priority of the structured data and the unstructured data, and when new content enters the knowledge graph, determining the coverage relationship according to the priority; constructing a visualization interface, visualizing the knowledge graph and displaying it on the visualization interface. The present invention solves the problem that the existing methods cannot combine the two together for knowledge management.

Description

Technical Field

[0001] The present invention belongs to the field of knowledge graph technology, and in particular relates to an enterprise knowledge management method, system, electronic device and storage medium. Background Art

[0002] Enterprise knowledge includes industry thesaurus, dictionary library, rule library, model library and industry consensus, etc. Most of them are managed by hierarchical catalog management. Knowledge from different sources and of different types faces challenges in updating, integration and management.

[0003] Existing knowledge management mainly adopts a hierarchical and classified management approach, combining various industry lexicons, dictionary libraries, rule libraries, model libraries, etc. into a knowledge catalog according to certain business logic, and storing the data in a database for query. Its disadvantages are insufficient semantic expression, simple knowledge association, and single dimension. Existing graph-based knowledge management mainly focuses on the engineering structured knowledge extraction part and entity recognition and extraction based on NLP technology, lacking an effective mechanism and method to combine the two parts together for knowledge management. Summary of the invention

[0004] The embodiments of the present application provide an enterprise knowledge management method, system, electronic device and storage medium to at least solve the problem that the existing enterprise knowledge management method cannot combine the two parts together to perform knowledge management.

[0005] In a first aspect, an embodiment of the present application provides a method for enterprise knowledge management, including: a structured data processing step, obtaining structured data in the enterprise knowledge, and making a first definition for the content to be graphed in the structured data, and generating a knowledge graph for the content to be graphed in the structured data according to the first definition; an unstructured data processing step, obtaining unstructured data in the enterprise knowledge, and making a second definition for the content to be graphed in the unstructured data, and adding the content to be graphed in the unstructured data into the knowledge graph according to the second definition; a priority strategy setting step, setting the priority of the structured data and the unstructured data, and when new content enters the knowledge graph, determining the coverage relationship according to the priority; a visualization interface construction step, constructing a visualization interface, visualizing the knowledge graph and displaying it on the visualization interface.

[0006] Preferably, the structured data processing step further comprises: analyzing the business field content in the structured data, and defining entities, relationship categories and attributes in the business field content.

[0007] Preferably, the unstructured data processing step further includes: analyzing the unstructured data through natural language processing technology, and constructing an extractor according to the second definition, extracting entities, relationship categories and attributes in the unstructured data through the extractor, and adding the extracted entities, relationship categories and attributes in the unstructured data into the knowledge graph.

[0008] Preferably, the method further includes a step of dynamically supplementing the knowledge graph: supplementing the knowledge graph according to a preset cycle, supplementing the structured data by incremental operation, and supplementing the unstructured data by full operation.

[0009] In the second aspect, an embodiment of the present application provides an enterprise knowledge management system, which is applicable to the above-mentioned enterprise knowledge management method, including: a structured data processing module, which obtains structured data in the enterprise knowledge, and makes a first definition for the content to be graphed in the structured data, and generates a knowledge graph for the content to be graphed in the structured data according to the first definition; an unstructured data processing module, which obtains unstructured data in the enterprise knowledge, and makes a second definition for the content to be graphed in the unstructured data, and adds the content to be graphed in the unstructured data into the knowledge graph according to the second definition; a priority strategy setting module, which sets the priority of the structured data and the unstructured data, and when new content enters the knowledge graph, determines the coverage relationship according to the priority; a visualization interface construction module, which constructs a visualization interface, visualizes the knowledge graph and displays it on the visualization interface.

[0010] In some of the embodiments, the structured data processing module further includes: analyzing business field content in the structured data, and defining entities, relationship categories and attributes in the business field content.

[0011] In some of the embodiments, the unstructured data processing module further includes: analyzing the unstructured data through natural language processing technology, and constructing an extractor according to the second definition, extracting entities, relationship categories and attributes in the unstructured data through the extractor, and adding the extracted entities, relationship categories and attributes in the unstructured data into the knowledge graph.

[0012] In some of the embodiments, the system further includes a knowledge graph dynamic supplement module: the knowledge graph is supplemented according to a preset cycle, the structured data is supplemented by incremental operation, and the unstructured data is supplemented by full operation.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an enterprise knowledge management method as described in the first aspect above is implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an enterprise knowledge management method as described in the first aspect above.

[0015] The present invention can be applied to the field of knowledge graph technology. Compared with related technologies, the embodiment of the present application adopts the method of industry graph library, making full use of the rich semantic expression ability of the graph, which is more suitable for storing and managing basic knowledge of enterprises with rich types, different standards and diverse sources. Specifically, graph-based knowledge management is performed in a visual way, which simplifies the operation and threshold of knowledge management; the process and rules for the fusion operation of the graph schema for structured data acquisition and the schema for unstructured data acquisition are defined, so that knowledge acquired from multiple channels can be effectively integrated from a mechanism perspective, increasing logical limitations and enhancing the implementation of knowledge. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a flow chart of the enterprise knowledge management method of the present invention;

[0018] Figure 2 A framework diagram of the enterprise knowledge management system of the present invention;

[0019] Figure 3 A framework diagram of an electronic device of the present invention;

[0020] In the above picture:

[0021] 1. Structured data processing module; 2. Unstructured data processing module; 3. Priority strategy setting module; 4. Knowledge graph dynamic supplement module; 5. Visual interface construction module; 60. Bus; 61. Processor; 62. Memory; 63. Communication interface. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0023] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0024] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0025] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a kind of", "the" and the like involved in this application do not indicate a quantitative limitation and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices.

[0026] The embodiment of the present application organizes and manages the knowledge within the enterprise in the form of a graph, and defines entities, relationships, and events of business concern.

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings:

[0028] Figure 1For the flow chart of the enterprise knowledge management method of the present invention, please refer to Figure 1 The enterprise knowledge management method of the present invention comprises the following steps:

[0029] S1: Obtain structured data in enterprise knowledge, and make a first definition of the content in the structured data that needs to be graphed, and generate a knowledge graph for the content in the structured data that needs to be graphed according to the first definition.

[0030] Optionally, the business field content in the structured data is analyzed, and the entities, relationship categories and attributes in the business field content are defined.

[0031] In the specific implementation, for structured data, by analyzing the business field content contained in the database table, the entities, relationship categories and their possible attributes included in the partial graph are defined, and the graph schema defined based on the structured data is imported.

[0032] In the specific implementation, since the construction of structured graphs is a complex engineering logic, in order to ensure the high consistency between the output results of the structured data graph generation work and the management knowledge in the knowledge base, in the knowledge base, the schema defined based on structured data only supports incremental synchronization, and does not support deletion and modification operations.

[0033] S2: Obtain the unstructured data in the enterprise knowledge, and make a second definition for the content in the unstructured data that needs to be graphed, and add the content in the unstructured data that needs to be graphed into the knowledge graph according to the second definition.

[0034] Optionally, the unstructured data is analyzed by natural language processing technology, and an extractor is constructed according to the second definition, and the entities, relationship categories and attributes in the unstructured data are extracted by the extractor, and the extracted entities, relationship categories and attributes in the unstructured data are added to the knowledge graph.

[0035] In the specific implementation, for unstructured data, through the comprehensive analysis of text data and entity recognition algorithm capabilities, an entity / relationship extractor is built to complete the supplement of graph entity, relationship category and attribute definitions, and synchronize the graph schema defined by unstructured data in the knowledge graph.

[0036] In specific implementation, when synchronizing the schema defined by unstructured data, if it is a brand new schema, add it directly; if it is a schema already involved in structured data, append the identifier information to the original schema definition.

[0037] In the specific implementation, for the graph part whose source is only unstructured data definition, manual addition, deletion and modification operations are supported. Users can add new entities on the page or modify the identifiers associated with the original entities based on the continuous enrichment and optimization of the identifiers; for the schema whose source is both structured and unstructured, only modification of the identifiers is supported, and deletion operations are not supported.

[0038] S3: Setting the priorities of the structured data and the unstructured data, and determining the coverage relationship according to the priorities when new content enters the knowledge graph.

[0039] In specific implementations, the data priority strategy can be set to either structured priority or unstructured priority, with higher priority data overwriting lower priority data.

[0040] S4: Supplement the knowledge graph according to a preset cycle, supplement the structured data by incremental operation, and supplement the unstructured data by full operation.

[0041] In the specific implementation, the knowledge extraction task supports scheduled and periodic execution. Structured data adopts an incremental operation mode to complete data append; unstructured data adopts a full operation mode and adopts a full coverage strategy for update.

[0042] S5: Construct a visualization interface, visualize the knowledge graph and display it on the visualization interface.

[0043] Figure 2 For a framework diagram of the enterprise knowledge management system according to the present invention, see Figure 2 ,include:

[0044] Structured data processing module 1: obtain structured data in enterprise knowledge, and make a first definition of the content in the structured data that needs to be graphed, and generate a knowledge graph for the content in the structured data that needs to be graphed according to the first definition.

[0045] Optionally, the business field content in the structured data is analyzed, and the entities, relationship categories and attributes in the business field content are defined.

[0046] In the specific implementation, for structured data, by analyzing the business field content contained in the database table, the entities, relationship categories and their possible attributes included in the partial graph are defined, and the graph schema defined based on the structured data is imported.

[0047] In the specific implementation, since the construction of structured graphs is a complex engineering logic, in order to ensure the high consistency between the output results of the structured data graph generation work and the management knowledge in the knowledge base, in the knowledge base, the schema defined based on structured data only supports incremental synchronization, and does not support deletion and modification operations.

[0048] Unstructured data processing module 2: obtain the unstructured data in the enterprise knowledge, and make a second definition for the content in the unstructured data that needs to be graphed, and add the content in the unstructured data that needs to be graphed into the knowledge graph according to the second definition.

[0049] Optionally, the unstructured data is analyzed by natural language processing technology, and an extractor is constructed according to the second definition, and the entities, relationship categories and attributes in the unstructured data are extracted by the extractor, and the extracted entities, relationship categories and attributes in the unstructured data are added to the knowledge graph.

[0050] In the specific implementation, for unstructured data, through the comprehensive analysis of text data and entity recognition algorithm capabilities, an entity / relationship extractor is built to complete the supplement of graph entity, relationship category and attribute definitions, and synchronize the graph schema defined by unstructured data in the knowledge graph.

[0051] In specific implementation, when synchronizing the schema defined by unstructured data, if it is a brand new schema, add it directly; if it is a schema already involved in structured data, append the identifier information to the original schema definition.

[0052] In the specific implementation, for the graph part whose source is only unstructured data definition, manual addition, deletion and modification operations are supported. Users can add new entities on the page or modify the identifiers associated with the original entities based on the continuous enrichment and optimization of the identifiers; for the schema whose source is both structured and unstructured, only modification of the identifiers is supported, and deletion operations are not supported.

[0053] Priority strategy setting module 3: setting the priorities of the structured data and the unstructured data, and determining the coverage relationship according to the priorities when new content enters the knowledge graph.

[0054] In specific implementations, the data priority strategy can be set to either structured priority or unstructured priority, with higher priority data overwriting lower priority data.

[0055] Knowledge graph dynamic supplement module 4: supplement the knowledge graph according to a preset period, supplement the structured data by incremental operation, and supplement the unstructured data by full operation.

[0056] In the specific implementation, the knowledge extraction task supports scheduled and periodic execution. Structured data adopts an incremental operation mode to complete data append; unstructured data adopts a full operation mode and adopts a full coverage strategy for update.

[0057] Visualization interface construction module 5: construct a visualization interface, visualize the knowledge graph and display it on the visualization interface.

[0058] In addition, combined Figure 1 The described enterprise knowledge management method can be implemented by an electronic device. Figure 3 It is a framework diagram of the electronic device of the present invention.

[0059] The electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0060] Specifically, the processor 61 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0061] Among them, the memory 62 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 62 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 62 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 62 may be inside or outside the data processing device. In a specific embodiment, the memory 62 is a non-volatile memory. In a specific embodiment, the memory 62 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable ProgrammableRead-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable ProgrammableRead-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0062] The memory 62 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 61 .

[0063] The processor 61 implements any one of the enterprise knowledge management methods in the above embodiments by reading and executing computer program instructions stored in the memory 62 .

[0064] In some of the embodiments, the electronic device may further include a communication interface 63 and a bus 60. Figure 3 As shown, the processor 61, the memory 62, and the communication interface 63 are connected via a bus 60 and communicate with each other.

[0065] The communication port 63 can realize data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0066] The bus 60 includes hardware, software or both, and couples the components of the electronic device to each other. The bus 60 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 60 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 60 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0067] The electronic device can execute an enterprise knowledge management method in an embodiment of the present application.

[0068] In addition, in combination with an enterprise knowledge management method in the above embodiment, the present application embodiment can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any enterprise knowledge management method in the above embodiment is implemented.

[0069] The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

[0070] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0071] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for enterprise knowledge management, characterized in that: include: A structured data processing step, obtaining structured data in enterprise knowledge, and performing a first definition on the content to be graphed in the structured data, and generating a knowledge graph for the content to be graphed in the structured data according to the first definition; The structured data processing step further includes: analyzing the business field content in the structured data, defining the entities, relationship categories and attributes in the business field content, and importing the graph schema defined based on the structured data; The unstructured data processing step obtains the unstructured data in the enterprise knowledge, and performs a second definition on the content to be graphed in the unstructured data, and adds the content to be graphed in the unstructured data into the knowledge graph according to the second definition; the unstructured data processing step further includes: analyzing the unstructured data by natural language processing technology, and constructing an extractor according to the second definition, extracting entities, relationship categories and attributes in the unstructured data by the extractor, adding the extracted entities, relationship categories and attributes in the unstructured data into the knowledge graph, and synchronizing the graph schema defined by the unstructured data in the knowledge graph. When synchronizing the schema defined by the unstructured data, if it is a brand new schema, it is directly added, and if it is a schema already involved in the structured data, the identifier information is appended to the original schema definition; A priority strategy setting step, setting the priorities of the structured data and the unstructured data, and determining the coverage relationship according to the priorities when new content enters the knowledge graph; A visualization interface construction step, constructing a visualization interface, visualizing the knowledge graph and displaying it on the visualization interface; The step of dynamically supplementing the knowledge graph supplements the knowledge graph according to a preset cycle, supplements the structured data through incremental operation, and supplements the unstructured data through full operation. The knowledge extraction task supports scheduled and periodic execution. The structured data adopts incremental operation to complete data appending; the unstructured data adopts full operation and adopts a full coverage strategy for update.

2. An enterprise knowledge management system, characterized in that: include: A structured data processing module obtains structured data in enterprise knowledge, and performs a first definition on the content to be graphed in the structured data, and generates a knowledge graph for the content to be graphed in the structured data according to the first definition; The structured data processing module further includes: analyzing the business field content in the structured data, defining the entities, relationship categories and attributes in the business field content, and importing the graph schema defined from the structured data; the unstructured data processing module obtains the unstructured data in the enterprise knowledge, and makes a second definition for the content to be graphed in the unstructured data, and adds the content to be graphed in the unstructured data into the knowledge graph according to the second definition; the unstructured data processing module further includes: analyzing the unstructured data by natural language processing technology, and constructing an extractor according to the second definition, extracting the entities, relationship categories and attributes in the unstructured data by the extractor, adding the extracted entities, relationship categories and attributes in the unstructured data into the knowledge graph, and synchronizing the graph sc defined by the unstructured data in the knowledge graph hema, when synchronizing the schema defined by unstructured data, if it is a brand new schema, it will be directly added; if it is a schema already involved in the structured data, the identifier information will be appended to the original schema definition; priority strategy setting module, setting the priority of the structured data and the unstructured data, when new content enters the knowledge graph, determining the coverage relationship according to the priority; visualization interface construction module, building a visualization interface, visualizing the knowledge graph and displaying it on the visualization interface; knowledge graph dynamic supplement module: according to a preset period, supplement the knowledge graph, supplement the structured data by incremental operation, supplement the unstructured data by full operation, the knowledge extraction task supports timed and periodic execution, structured data adopts incremental operation to complete data appending; unstructured data adopts full operation and adopts full coverage strategy for update.

3. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the enterprise knowledge management method according to claim 1 is implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the enterprise knowledge management method according to claim 1 is implemented.

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