Enterprise-level demand knowledge graph construction method, apparatus and device, and computer program product

Abstract

The invention discloses an enterprise-level demand knowledge graph construction method, device and equipment and a computer program product, and the method comprises the steps: obtaining source data of an enterprise-level demand knowledge graph, including information engineering data, business demand documents and expert database data; extracting entity data and relational data of the enterprise-level demand knowledge graph from the source data; generating graph data according to the entity data and the relational data and storing the graph data in a graph database; and post-processing the graph data based on the graph database to obtain the enterprise-level demand knowledge graph. According to the method, the enterprise-level demand knowledge graph is constructed by utilizing the multi-source data of the enterprise for enterprise-level complex demands, the method has efficient and visual demand tool properties, teams can be helped to clearly display the association relationship among the demands, and the work and cooperation efficiency is remarkably improved. Through the demand knowledge graph, team members can better understand the logic relation and the dependency relation between the demands, and accurate management and effective control of the demands are achieved.

Description

Technical Field

[0001] The present application relates to the field of knowledge graph technology, and in particular to a method, device and equipment, and a computer program product for constructing an enterprise-level demand knowledge graph. Background Art

[0002] With the increase of business systems and the continuous expansion of functions, the interdependence and influence of requirements are becoming increasingly complex, making it difficult for traditional management methods to efficiently identify, track and handle the addition and change of requirements and the chain reactions they bring. This is specifically manifested in the following aspects:

[0003] 1) Unclear demand relationships: In a multi-business system, the requirements between different systems and within the system are often intricately related. There is a lack of effective tools or methods to intuitively display these relationships, making it difficult for the team to fully and accurately recognize and understand the requirements.

[0004] 2) Difficulty in assessing the impact of changes: Any addition or change in requirements may have an impact on multiple systems or functional modules. Traditional methods make it difficult to quickly and accurately assess these impacts, leading to increased risks such as project delays, cost overruns, or reduced system stability.

[0005] 3) Inefficient team collaboration: Since demand relationships are complex and difficult to grasp, information asymmetry is prone to occur during communication and collaboration between business, project, and technical teams, leading to slow decision-making and poor execution, which in turn affects overall work efficiency.

[0006] 4) Increased business risks and system operation risks: Failure to effectively manage and predict the risks brought about by changes in demand may lead to serious consequences such as business logic errors, inconsistent data between systems, and service interruptions, which will damage corporate interests and user experience. Summary of the invention

[0007] The embodiments of the present application provide a method, device, equipment, and computer program product for constructing an enterprise-level demand knowledge graph to realize the construction of an enterprise-level demand knowledge graph and improve the efficiency of enterprise-level demand research and development and management.

[0008] The present application embodiment adopts the following technical solutions:

[0009] In a first aspect, an embodiment of the present application provides a method for constructing an enterprise-level demand knowledge graph, and the method for constructing an enterprise-level demand knowledge graph includes:

[0010] Acquire source data of the enterprise-level demand knowledge graph, wherein the source data includes information engineering data, business demand documents, and expert database data;

[0011] Extracting entity data and relationship data of the enterprise-level demand knowledge graph from source data of the enterprise-level demand knowledge graph;

[0012] Generate graph data based on the entity data and relationship data of the enterprise-level demand knowledge graph and store it in a graph database;

[0013] The graph data is post-processed based on the graph database to obtain an enterprise-level demand knowledge graph.

[0014] Optionally, extracting entity data and relationship data of the enterprise-level demand knowledge graph from source data of the enterprise-level demand knowledge graph includes:

[0015] Extracting first entity data and first relationship data from the information engineering data, wherein the first entity data includes at least one first entity in a system and an engineering and corresponding attribute data, and the first relationship data includes the relationship between the first entities and the corresponding attribute data;

[0016] Extracting second entity data and second relationship data from the business requirement document, the second entity data including at least one second entity among a function point, a function item, and a function module and corresponding attribute data, the second relationship data including a relationship between the second entities and the corresponding attribute data and a relationship between the second entity and the first entity and the corresponding attribute data;

[0017] Multiple third entity data and third relationship data are extracted from the expert database data, wherein the third entity data includes at least one third entity among review experts, professional fields and business segments and corresponding attribute data, and the third relationship data includes the relationship between third entities and corresponding attribute data, as well as the relationship between the third entity and the second entity, the first entity and the corresponding attribute data.

[0018] Optionally, generating graph data according to entity data and relationship data of the enterprise-level demand knowledge graph and storing it in a graph database includes:

[0019] Converting the entity data and relationship data of the enterprise-level demand knowledge graph into structured entity data tables and relationship data tables respectively;

[0020] Configuring a mapping relationship between the structured entity data table and the relationship data table and the graph structure, and converting the structured entity data table and the relationship data table into graph data;

[0021] The graph data is stored in a graph database.

[0022] Optionally, the entity data and relationship data of the enterprise-level demand knowledge graph include first entity data and first relationship data, second entity data and second relationship data, and third entity data and third relationship data, and converting the entity data and relationship data of the enterprise-level demand knowledge graph into structured entity data tables and relationship data tables, respectively, includes:

[0023] Aggregating the first entity data, the second entity data, and the third entity data to generate a structured entity data table;

[0024] The first relational data, the second relational data, and the third relational data are aggregated to generate a structured relational data table.

[0025] Optionally, the entity data includes basic attributes and derived attributes of the entity, the attribute values ​​of the basic attributes of the entity contained in the graph data are directly extracted from the source data, and the post-processing of the graph data based on the graph database to obtain the enterprise-level demand knowledge graph includes:

[0026] According to the entity data and relationship data associated with the derived attributes in the graph data, the attribute value of the derived attributes of the entity is calculated, the entity data associated with the derived attributes includes review experts, and the derived attributes of the entity include the cumulative number of reviews by the review experts.

[0027] Optionally, the method for constructing the enterprise-level demand knowledge graph further includes:

[0028] Receive enterprise-level demand knowledge retrieval requests;

[0029] According to the enterprise-level demand knowledge retrieval request, a search is performed in the enterprise-level demand knowledge graph to obtain an enterprise-level demand knowledge retrieval result.

[0030] Optionally, the enterprise-level demand knowledge retrieval request includes at least one of an engineering information retrieval request, a business demand information retrieval request, and an expert information retrieval request. The engineering information retrieval request is used to retrieve engineering basic data and engineering related data, the business demand information retrieval request is used to retrieve the impact scope of business demand changes, and the expert information retrieval request is used to retrieve expert basic data and expert related data.

[0031] In a second aspect, an embodiment of the present application further provides a device for constructing an enterprise-level demand knowledge graph, wherein the device for constructing an enterprise-level demand knowledge graph comprises:

[0032] An acquisition unit, used to acquire source data of an enterprise-level demand knowledge graph, wherein the source data includes information engineering data, business demand documents, and expert database data;

[0033] An extraction unit, used to extract entity data and relationship data of the enterprise-level demand knowledge graph from the source data of the enterprise-level demand knowledge graph;

[0034] A generating unit, configured to generate graph data according to the entity data and relationship data of the enterprise-level demand knowledge graph and store the generated graph data in a graph database;

[0035] A post-processing unit is used to post-process the graph data based on the graph database to obtain an enterprise-level demand knowledge graph.

[0036] In a third aspect, an embodiment of the present application further provides a device, including:

[0037] A processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to execute any of the aforementioned methods for constructing an enterprise-level demand knowledge graph.

[0038] In a fourth aspect, an embodiment of the present application further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements any of the aforementioned methods for constructing an enterprise-level demand knowledge graph.

[0039] At least one of the above technical solutions adopted in the embodiment of the present application can achieve the following beneficial effects: the method for constructing the enterprise-level demand knowledge graph in the embodiment of the present application first obtains the source data of the enterprise-level demand knowledge graph, and the source data includes information engineering data, business demand documents and expert library data; then extracts the entity data and relationship data of the enterprise-level demand knowledge graph from the source data of the enterprise-level demand knowledge graph; then generates graph data based on the entity data and relationship data of the enterprise-level demand knowledge graph and stores it in the graph database; finally, post-processes the graph data based on the graph database to obtain the enterprise-level demand knowledge graph. The method for constructing the enterprise-level demand knowledge graph in the embodiment of the present application is oriented to the complex needs of the enterprise level, and constructs the enterprise-level demand knowledge graph using the multi-source data of the enterprise. It has the characteristics of an efficient and intuitive demand tool, which can help the team clearly display the correlation between needs, quickly identify and judge the impact of demand changes, business modifications, and new functions, thereby reducing business risks and system operation risks, and significantly improving work and collaboration efficiency. Through the demand knowledge graph, team members can better understand the logical relationship and dependency relationship between needs, and realize accurate management and effective control of needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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:

[0041] Figure 1 A flowchart of a method for constructing an enterprise-level demand knowledge graph according to an embodiment of the present application;

[0042] Figure 2 This is a schematic diagram of the structure of an enterprise-level demand knowledge graph in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the structure of a device for constructing an enterprise-level demand knowledge graph in an embodiment of the present application;

[0044] Figure 4 This is a schematic diagram of the structure of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0046] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0047] The main technical terms involved in this application include:

[0048] 1) Knowledge Graph: Knowledge graph is a large-scale semantic network that aims to help computer systems understand the real world more deeply by representing real-world objects and their relationships in a network structure. Knowledge graph provides a way to better organize, manage and understand massive amounts of information.

[0049] 2) Directed Graph: A directed graph is an important concept in graph theory, used to describe directed relationships between objects or things. A directed graph is a graph consisting of a set of vertices (or nodes) and directed edges connecting these vertices. Each edge has a direction, usually represented as pointing from one vertex to another.

[0050] 3) Entity: Entity is one of the core concepts of knowledge graph. Entity can be anything in the real world, such as people, companies, telephone numbers, email addresses, addresses, etc. Entities correspond to nodes in directed graphs.

[0051] 4) Relationship: Relationship is the relationship between things in the objective world, such as the relationship between people and enterprises (including legal representatives, senior executives, guarantors, etc.), the relationship between bank account transfer transactions, etc. Relationships correspond to edges in a directed graph.

[0052] 5) Attributes: Attributes are universal characteristic descriptions of entities and relationships, such as age and gender attributes of a person, investment time and investment amount attributes of an investment relationship, etc.

[0053] 6) Graph Database: A database system that uses graph structure for data storage and query.

[0054] The present application embodiment provides a method for constructing an enterprise-level demand knowledge graph, such as Figure 1 As shown, a flow chart of a method for constructing an enterprise-level demand knowledge graph in an embodiment of the present application is provided, and the method for constructing an enterprise-level demand knowledge graph at least includes the following steps S110 to S140:

[0055] Step S110, obtaining source data of the enterprise-level demand knowledge graph, wherein the source data includes information engineering data, business demand documents, and expert library data.

[0056] When building an enterprise-level demand knowledge graph, you need to first export the source data required for knowledge graph construction from the enterprise's data source system, mainly including information engineering data, business requirements documents, and expert database data. Information engineering data mainly includes system and engineering data, business requirements documents are mainly requirements documents written for information engineering, and expert database data mainly includes expert information used to review business requirements documents.

[0057] It should be noted that the above-mentioned enterprise can be any enterprise that has the above-mentioned source data and has the need to build an enterprise-level demand knowledge graph, for example, it can be a banking institution, and the banking institution can export the above-mentioned source data from its own data source system.

[0058] Step S120, extracting entity data and relationship data of the enterprise-level demand knowledge graph from the source data of the enterprise-level demand knowledge graph.

[0059] Combination Figure 2 , a schematic diagram of the structure of an enterprise-level demand knowledge graph in an embodiment of the present application is provided. Knowledge graph is a special method of knowledge storage and representation, usually represented by a large-scale semantic web, entity-relationship-entity link topological structure, and its main components are entities, relationships and attributes. According to the structural design of the knowledge graph, the various source data obtained in the above steps are processed, and entity data and relationship data for constructing an enterprise-level demand knowledge graph can be extracted therefrom. The entity data may include various related entities and their attribute data contained in the enterprise architecture, and the relationship data may include the relationship between entities and their attribute data.

[0060] Step S130, generating graph data based on the entity data and relationship data of the enterprise-level demand knowledge graph and storing it in a graph database.

[0061] After extracting the entity data and relationship data, the graph data of the enterprise-level demand knowledge graph can be generated using the entity data and relationship data according to the graph structure of the knowledge graph. This process mainly includes the preliminary construction of all entities and entity attributes contained in the enterprise-level demand knowledge graph, as well as the relationships and relationship attributes between all entities.

[0062] The graph database generated above can be stored in the graph database as a basis for subsequent retrieval and analysis.

[0063] Step S140, post-processing the graph data based on the graph database to obtain an enterprise-level demand knowledge graph.

[0064] Considering that some specific attribute information such as entity attributes or entity relationship attributes contained in some enterprise-level demand knowledge graphs cannot be directly extracted from the source data, the embodiment of this application needs to be based on the graph database to further process the existing graph data in the graph database, so as to obtain complete enterprise-level demand knowledge graph information, and then complete the construction of the enterprise-level demand knowledge graph. Subsequently, according to the needs of different application scenarios, the data in the enterprise-level demand knowledge graph can be retrieved and analyzed to quickly obtain the required information.

[0065] The construction of the enterprise-level demand knowledge graph of the embodiment of the present application can be obtained by using the enterprise's knowledge graph system. The knowledge graph system uses a graph database as a storage medium, and the upper layer has the capabilities of data processing, knowledge modeling, knowledge processing, and knowledge application.

[0066] The method for constructing an enterprise-level demand knowledge graph in the embodiment of the present application is oriented to complex enterprise-level demands. It uses the enterprise's multi-source data to construct an enterprise-level demand knowledge graph, which has the characteristics of an efficient and intuitive demand tool, and can help the team clearly display the relationship between demands, quickly identify and judge the impact of demand changes, business modifications, and new functions, thereby reducing business risks and system operation risks, and significantly improving work and collaboration efficiency. Through the demand knowledge graph, team members can better understand the logical relationships and dependencies between demands, and achieve accurate management and effective control of demands.

[0067] In some embodiments of the present application, extracting entity data and relationship data of the enterprise-level demand knowledge graph from the source data of the enterprise-level demand knowledge graph includes: extracting first entity data and first relationship data from the information engineering data, the first entity data including at least one first entity and corresponding attribute data in the system and the engineering, and the first relationship data including the relationship between the first entities and the corresponding attribute data; extracting second entity data and second relationship data from the business requirement document, the second entity data including at least one second entity and corresponding attribute data in function points, function items and function modules, the second relationship data including the relationship between the second entities and the corresponding attribute data as well as the relationship between the second entity and the first entity and the corresponding attribute data; extracting multiple third entity data and third relationship data from the expert library data, the third entity data including at least one third entity and corresponding attribute data in review experts, professional fields and business segments, the third relationship data including the relationship between the third entities and the corresponding attribute data as well as the relationship between the third entity and the second entity and the first entity and the corresponding attribute data.

[0068] The entity data used to construct an enterprise-level demand knowledge graph in the embodiment of the present application mainly includes entities and their corresponding attributes, as well as data such as the relationships between entities and relationship attributes. The source data types of the enterprise-level demand knowledge graph are different, and the corresponding entity data and relationship data obtained are also different. Therefore, the embodiment of the present application defines three major categories of entity data and relationship data for the three main source data types: information engineering data, business demand documents, and expert library data.

[0069] Continue to refer Figure 2 For the information engineering data source, the information engineering data maintained in the organization's information technology management system can be used as the basis. The information engineering data includes system information, engineering information, department information, etc., which are used to extract entities such as systems and projects and their attribute data, as well as to extract the relationship between systems and projects and their attribute data. For example, the relationship can be a main system relationship or an associated system relationship.

[0070] For the data source of business requirement documents, each information project needs to write a business requirement document to specify the business requirement scope, business process, business functions and non-functional requirements. The requirement document has a unified format and content requirements, including information such as requirement background, requirement description, and related systems. Therefore, entities such as function points, function items, and function modules and their attribute data can be extracted from the business requirement document, as well as the relationship between function points, function items, and function modules and their attribute data. Since the business requirement document is written for each information project, the relationship between entities such as function points, function items, and function modules and the project can also be extracted.

[0071] For the data source of expert database data, the basis is that the enterprise has set up a business requirements review expert database, and the members must be formal employees of the enterprise who have passed relevant certifications. Experts in the database are classified according to their professional knowledge fields, and experts can be invited to review business requirements documents in their respective fields. Therefore, data such as expert information, field information, and review information can be extracted from the expert database to extract entities such as review experts, professional fields, business segments, and their attribute data, as well as related relationships, such as the relationship between review experts and professional fields, and the review relationship between engineering and review experts.

[0072] It should be noted that the relationship between entities defined in the embodiments of the present application includes both direct relationships that can be extracted from source data and indirect relationships determined on the basis of direct relationships extracted from source data. For example, since the business requirement document is written for each project, there is a direct correlation between the functional information extracted from the business requirement document and the project, and the review experts in the expert database review the business requirement document, so there is a direct correlation between the review expert information and the functional information extracted from the business requirement document. Based on these two direct correlations, it can be further determined that there is an indirect correlation between the review expert information and the project.

[0073] In some embodiments of the present application, generating graph data based on the entity data and relationship data of the enterprise-level demand knowledge graph and storing them in a graph database includes: converting the entity data and relationship data of the enterprise-level demand knowledge graph into structured entity data tables and relationship data tables, respectively; configuring a mapping relationship between the structured entity data tables and relationship data tables and the graph structure, converting the structured entity data tables and relationship data tables into graph data; and storing the graph data in a graph database.

[0074] By processing the source data according to the structural design of the knowledge graph, entities, relationships, and attribute fields can be extracted, and then the extracted entities and their attribute field information can be converted into entity data tables, and the extracted relationships and their attribute field information can be converted into structured data of relationship data tables.

[0075] This embodiment of the application takes a banking institution as an example and further designs the main graph structure of the enterprise-level demand knowledge graph, as shown in Table 1 and Table 2 below:

[0076] Table 1 Entities and their attributes selection

[0077]

[0078]

[0079] Table 2 Relationships and their attribute selection

[0080]

[0081]

[0082] Based on the above graph structure design, by configuring the mapping relationship between structured entity table data and relationship table data and graph structure design, attribute search support and other information, the structured entity table data and relationship table data can be converted into graph data and stored in the graph database.

[0083] In some embodiments of the present application, the entity data and relationship data of the enterprise-level demand knowledge graph include first entity data and first relationship data, second entity data and second relationship data, and third entity data and third relationship data, and converting the entity data and relationship data of the enterprise-level demand knowledge graph into structured entity data tables and relationship data tables respectively includes: aggregating the first entity data, the second entity data, and the third entity data to generate a structured entity data table; aggregating the first relationship data, the second relationship data, and the third relationship data to generate a structured relationship data table.

[0084] Based on the aforementioned embodiment, the first entity data and the first relationship data can be extracted from the information engineering data, the second entity data and the second relationship data can be extracted from the business requirement document, and the second entity data and the second relationship data can be extracted from the expert database data. The first entity data, the second entity data, and the third entity data are all entity data required to construct an enterprise-level demand knowledge graph, so the first entity data, the second entity data, and the third entity data can be summarized into an entity data table, and the entity data table is used to uniformly store and maintain all entity-related information. The first relationship data, the second relationship data, and the third relationship data are all relationship data required to construct an enterprise-level demand knowledge graph, so the first relationship data, the second relationship data, and the third relationship data can be summarized into a relationship data table, and the relationship data table is used to uniformly store and maintain all entity-related information.

[0085] All entity data in the above entity data table and all relationship data in the relationship data table serve as the basic data for the subsequent construction of the enterprise-level demand knowledge graph.

[0086] In some embodiments of the present application, the entity data includes basic attributes and derived attributes of the entity, the attribute values ​​of the basic attributes of the entity contained in the graph data are directly extracted from the source data, and the post-processing of the graph data based on the graph database to obtain the enterprise-level demand knowledge graph includes: calculating the attribute values ​​of the derived attributes of the entity according to the entity data and relationship data associated with the derived attributes in the graph data, the entity data associated with the derived attributes includes review experts, and the derived attributes of the entity include the cumulative number of reviews by the review experts.

[0087] The entity attribute information contained in the entity data of the embodiment of the present application can be divided into basic attributes and derived attributes of the entity. The basic attributes can be understood as attributes that can directly extract specific attribute information from the source data. For example, attribute information such as the project name and project number of the engineering entity can be directly extracted from the information engineering data, attribute information such as the number and name of the function point entity can be directly extracted from the business requirement document, and attribute information such as the name and department of the review expert entity can be directly extracted from the expert database data.

[0088] Derived attributes can be understood as attributes whose specific attribute information cannot be directly extracted from the source data. For example, the cumulative number of reviews of the review expert entity extracted from the expert database data is a derived attribute. The cumulative number of reviews requires further statistics on the review records of the review expert in the graph database, and the number of business requirement documents of all projects that have been reviewed by the review expert is added up.

[0089] In some embodiments of the present application, the method for constructing the enterprise-level demand knowledge graph also includes: receiving an enterprise-level demand knowledge retrieval request; searching in the enterprise-level demand knowledge graph according to the enterprise-level demand knowledge retrieval request to obtain an enterprise-level demand knowledge retrieval result.

[0090] Based on the enterprise-level demand knowledge graph constructed based on the aforementioned embodiments, the data in the enterprise-level demand knowledge graph can be retrieved and analyzed according to the needs of different application scenarios. Due to the structural characteristics of the knowledge graph itself and the mapping relationship between the structured entity data table and relationship data table established in this application and the graph structure of the enterprise-level demand knowledge graph, the required information can be quickly retrieved during the search, which improves the search efficiency and meets the usage requirements of different scenarios.

[0091] In some embodiments of the present application, the enterprise-level demand knowledge retrieval request includes at least one of an engineering information retrieval request, a business demand information retrieval request, and an expert information retrieval request. The engineering information retrieval request is used to retrieve engineering basic data and engineering related data, the business demand information retrieval request is used to retrieve the impact scope of business demand changes, and the expert information retrieval request is used to retrieve expert basic data and expert related data.

[0092] Based on the enterprise-level demand knowledge graph constructed in the above embodiment, the embodiment of the present application mainly helps demand managers manage and understand demand associations across systems and within systems from the following three application scenarios:

[0093] First, the project information retrieval scenario. Through the project information retrieval request, search in the enterprise-level demand knowledge graph to quickly obtain relevant information. For example, by searching for a project, you can get the basic information of the project, the distribution of current project requirements, related system information, etc. Further expansion of the enterprise-level demand knowledge graph can obtain the previous projects and demand information of the system to which the project belongs (search for project name and other information).

[0094] Second, the impact scope analysis scenario of demand changes. Through the business demand information retrieval request, search and association analysis are performed in the enterprise-level demand knowledge graph to determine the impact scope of demand changes. For example, when a certain function is adjusted, the demand is searched on the knowledge graph, and the associated systems that may be affected are quickly located through the association relationship between the demands.

[0095] Third, expert information retrieval scenario. Before a project is submitted for business requirements review, the relevant information of the expert can be searched in the enterprise-level requirements knowledge graph based on the expert information retrieval request to obtain the expert's basic information and participation in the business requirements review, which helps to better evaluate the expert's qualifications and the matching degree with the project to be reviewed, thereby improving the management efficiency of the review experts and the quality and efficiency of the matching work.

[0096] The knowledge graph system of this application is technically implemented based on a microservice architecture and consists of different functional layers, including:

[0097] The front-end interface uses the HTML+CSS+JS technology stack, and uses React and NodeJS for page development. Users send requests to the Nginx of the management portal through the HTTP protocol. Nginx displays front-end resources and forwards back-end APIs, and uses HTTP+Json communication to transmit data objects. React is efficient and fast, with high cross-browser compatibility. It uses a declarative component mechanism to build a colorful user interface, which can provide users with a better user experience;

[0098] The application layer uses various open source components to compile work chains, such as the registration center (Eureka), configuration center (Apollo), and call chain monitoring (PinPoint or CAT) provided by the service to achieve registration, management, and monitoring between modules. The presentation layer and the business layer are completely separated logically through Zuul gateway connection + JWT for identity authentication. Service calls within the system use Rest style for communication connections, and Ribbon is used to achieve load balancing between services. The container also includes DAS (Direct-Attached Storage) database access, Log log, WS (Web Service) interface, and SLA (Service-Level Agreement) service level agreement as quality assurance.

[0099] For complex workflows, we use the platform BPS to control the process flow. We use the report tools provided by the platform to perform simple / complex report statistics. We use ELK technology to implement logs, and receive external system messages through the message middleware for data synchronization notification. In terms of software technology architecture, we use object relationships and mapping to implement database operations using a combination of JDBCTemplate and Ibatis.

[0100] The knowledge storage layer uses the current mainstream data processing Hadoop ecosystem combined with JanusGraph. The main components are Postgresql+JanusGraph+HBase+Hive+Redis+ElasticSearch, which meets the needs of database master and backup, knowledge computing, knowledge processing, etc. As a graph computing engine, JanusGraph combines various Hadoop components to realize the ability to analyze and process graph data.

[0101] In the knowledge computing layer, service configuration data is centrally managed and distributed through Zookeeper, and YARN resource management is scheduled through Livy to achieve batch data processing of online business data and related system data through Spark, and the processing result data is synchronized to JanusGraph graph data. Data can also be extracted and stored in full-text retrieval ES, column database Hbase, etc. as needed.

[0102] When in use, the column cluster data tables such as entities and relationships used in the knowledge graph will first be stored in the Hive library. When running graph processing and computing tasks, Hadoop calls the Spark component to process the data. The group mining results are generally stored in Hive in the form of entity and relationship tables. The table structure is stored in the form of groups, recording information such as the group id and the entity number in the group. The indicator calculation results are generally stored in Hbase, which generally records the entity uid and the indicator calculation results. The data of the verification center and the early warning center will be stored in the ElasticSearch database for quick retrieval. Graph visualization data is generally stored in JanusGraph, and the table headers in the column cluster data table need to be mapped to the entity relationships in the graph database. In this application, the entity data table has 8 types of entities, system, engineering, review expert, function point, professional field, business segment, function item, function module, and the relationship data table has 8 relationships, connecting the above entities to each other.

[0103] To summarize, the method for constructing the enterprise-level demand knowledge graph of this application is based on the information system management architecture of banking institutions, including various systems and requirements in dimensions such as project management, business needs, and expert database.

[0104] The overall idea of ​​constructing an enterprise-level demand knowledge graph in this application mainly includes the extraction of entities, relationships, and attributes. The enterprise-level demand knowledge graph framework constructed using the overall idea of ​​this application is conducive to banks' demand management work and improves the work efficiency of demand management personnel in the following aspects:

[0105] First, quickly grasp relevant information through graph search. For example, by searching for projects, you can get the construction status of the project, demand distribution, related system information, etc.; second, determine the impact scope of demand changes through graph association analysis. For example, when a function is adjusted, quickly locate the related systems that may be affected through the association relationship; third, by searching for experts, evaluate the expert's qualifications and the degree of matching with the project to be reviewed based on the expert's basic information and participation in business demand reviews.

[0106] This application is aimed at complex enterprise-level requirements. The requirement knowledge graph (model) constructed by this application has the characteristics of an efficient and intuitive requirement tool, which can help the team clearly display the relationship between requirements, quickly identify and judge the impact of requirement changes, business modifications, and new functions, thereby reducing business risks and system operation risks, and significantly improving work and collaboration efficiency. Through the requirement knowledge graph, team members can better understand the logical relationship and dependency relationship between requirements, and achieve accurate management and effective control of requirements.

[0107] The present application embodiment also provides a device 300 for constructing an enterprise-level demand knowledge graph, such as Figure 3As shown, a schematic diagram of the structure of a device for constructing an enterprise-level demand knowledge graph in an embodiment of the present application is provided. The device 300 for constructing an enterprise-level demand knowledge graph includes:

[0108] An acquisition unit 310 is used to acquire source data of an enterprise-level demand knowledge graph, wherein the source data includes information engineering data, business demand documents, and expert database data;

[0109] An extraction unit 320, configured to extract entity data and relationship data of the enterprise-level demand knowledge graph from source data of the enterprise-level demand knowledge graph;

[0110] A generating unit 330, configured to generate graph data according to the entity data and relationship data of the enterprise-level demand knowledge graph and store the graph data in a graph database;

[0111] The post-processing unit 340 is used to post-process the graph data based on the graph database to obtain an enterprise-level demand knowledge graph.

[0112] In some embodiments of the present application, the extraction unit 320 is specifically used to: extract first entity data and first relationship data from the information engineering data, the first entity data including at least one first entity and corresponding attribute data in the system and the engineering, and the first relationship data including the relationship between the first entities and the corresponding attribute data; extract second entity data and second relationship data from the business requirement document, the second entity data including at least one second entity and corresponding attribute data in function points, function items and function modules, the second relationship data including the relationship between the second entities and the corresponding attribute data as well as the relationship between the second entity and the first entity and the corresponding attribute data; extract multiple third entity data and third relationship data from the expert library data, the third entity data including at least one third entity and corresponding attribute data in review experts, professional fields and business segments, the third relationship data including the relationship between the third entities and the corresponding attribute data as well as the relationship between the third entity and the second entity and the first entity and the corresponding attribute data.

[0113] In some embodiments of the present application, the generation unit 330 is specifically used to: convert the entity data and relationship data of the enterprise-level demand knowledge graph into structured entity data tables and relationship data tables, respectively; configure the mapping relationship between the structured entity data tables and relationship data tables and the graph structure, and convert the structured entity data tables and relationship data tables into graph data; and store the graph data in a graph database.

[0114] In some embodiments of the present application, the entity data and relationship data of the enterprise-level demand knowledge graph include first entity data and first relationship data, second entity data and second relationship data, and third entity data and third relationship data, and the generation unit is specifically used to: summarize the first entity data, the second entity data, and the third entity data to generate a structured entity data table; summarize the first relationship data, the second relationship data, and the third relationship data to generate a structured relationship data table.

[0115] In some embodiments of the present application, the entity data includes basic attributes and derived attributes of the entity, and the attribute values ​​of the basic attributes of the entity contained in the graph data are directly extracted from the source data. The post-processing unit 340 is specifically used to: calculate the attribute values ​​of the derived attributes of the entity according to the entity data and relationship data associated with the derived attributes in the graph data, the entity data associated with the derived attributes include review experts, and the derived attributes of the entity include the cumulative number of reviews by the review experts.

[0116] In some embodiments of the present application, the device 300 for constructing the enterprise-level demand knowledge graph also includes: a receiving unit for receiving an enterprise-level demand knowledge retrieval request; and a retrieval unit for searching in the enterprise-level demand knowledge graph according to the enterprise-level demand knowledge retrieval request to obtain an enterprise-level demand knowledge retrieval result.

[0117] In some embodiments of the present application, the enterprise-level demand knowledge retrieval request includes at least one of an engineering information retrieval request, a business demand information retrieval request, and an expert information retrieval request. The engineering information retrieval request is used to retrieve engineering basic data and engineering related data, the business demand information retrieval request is used to retrieve the impact scope of business demand changes, and the expert information retrieval request is used to retrieve expert basic data and expert related data.

[0118] It can be understood that the above-mentioned enterprise-level demand knowledge graph construction device can implement the various steps of the enterprise-level demand knowledge graph construction method provided in the aforementioned embodiment. The relevant explanations on the enterprise-level demand knowledge graph construction method are applicable to the enterprise-level demand knowledge graph construction device and will not be repeated here.

[0119] Figure 4 Schematic diagram of the structure of a device in the embodiment of the present application. Figure 4 As shown, the device includes one or more processors (or processing units), may further include one or more memories coupled to the processors, and may further include a communication module coupled to the processors.

[0120] The communication module can be used to communicate with other devices or apparatuses, such as the transmission or reception of data and / or signals. The communication module can have at least one communication module for communication. The communication module can include any interface necessary for communicating with other devices. Exemplarily, the communication module can be a transceiver, a circuit, a bus, a module, or other types of communication modules.

[0121] The processor may include, but is not limited to, at least one of the following: a general-purpose computer, a special-purpose computer, a microcontroller, a digital signal controller (DSP), or one or more of a controller-based multi-core controller architecture. The device may have multiple processors, such as application-specific integrated circuit chips, which are time-dependent and synchronized with a clock of a main processor.

[0122] The memory may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, at least one of the following: read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD), or other magnetic storage and / or optical storage. Examples of volatile memories include, but are not limited to, at least one of the following: random access memory (RAM), or other volatile memories that do not persist during the duration of a power outage.

[0123] The computer program includes computer executable instructions executed by an associated processor. The program can be stored in ROM. The processor can perform any suitable actions and processes by loading the program into RAM.

[0124] The possible implementation of the present application can be implemented by means of a program, so that the communication device can perform any process discussed in the above embodiments. The possible implementation of the present application can also be implemented by hardware or by a combination of software and hardware.

[0125] In some embodiments, the program may be tangibly contained in a computer-readable storage medium, which may be included in the device (such as in a memory) or other storage device accessible by the device. The program may be loaded from the computer-readable storage medium to the RAM for execution. The computer-readable storage medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc.

[0126] The present application embodiment also provides a computer-readable storage medium, on which computer instructions or program codes are stored, and when the processor runs the instructions or the program codes, the processor executes the methods and functions involved in any of the above embodiments. Computer-readable media can be any tangible medium containing or storing programs for or related to instruction execution systems, devices or equipment. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any suitable combination thereof. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrations. More detailed examples of computer-readable storage media include electrical connections with one or more wires, magnetic media (e.g., disks, floppy disks, hard disks, tapes, magnetic storage devices), optical media (e.g., optical storage devices, DVDs), semiconductor media (e.g., solid-state hard drives), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), or any suitable combination thereof, etc.

[0127] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The embodiment of the present application also provides at least one computer program product tangibly stored on a non-temporary computer-readable storage medium. The computer program product includes one or more computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process, method and function involved in any of the above embodiments. When the computer program instruction is loaded and executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instruction can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0128] The present application embodiment also proposes a computer program product, including a computer program or instruction, when the computer program or instruction is run on a computer, the computer is made to perform the process, method and function in the above-mentioned embodiment. Usually, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or realize specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0129] In general, various embodiments of the present application may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be performed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flow charts, or using some other graphical representations, it should be understood that the boxes, devices, systems, techniques, or methods described herein may be implemented as, for example, non-limiting examples, hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0130] It should be noted that although the embodiments of the present application are described above in conjunction with the accompanying drawings, the above embodiments are not independent of each other, and they can also be combined to obtain other embodiments. The division of the modes, situations, categories and embodiments in the embodiments of the present application is only for the convenience of description and should not constitute a special limitation. The features in the various modes, categories, situations and embodiments can be combined with each other in a logical manner. The various implementation methods of the present application can be combined arbitrarily to achieve different technical effects. The embodiments of the present application no longer list various combinations.

[0131] In addition, although the operation of the method of the present disclosure is described in a particular order in the accompanying drawings, this does not require or imply that these operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flow chart can change the order of execution. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution. It should also be noted that the features and functions of two or more devices according to the present disclosure can be embodied in one device. Conversely, the features and functions of a device described above can be further divided into being embodied by multiple devices.

[0132] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0133] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for constructing an enterprise-level demand knowledge graph, characterized in that: The method for constructing the enterprise-level demand knowledge graph includes: Acquire source data of the enterprise-level demand knowledge graph, wherein the source data includes information engineering data, business demand documents, and expert database data; Extracting entity data and relationship data of the enterprise-level demand knowledge graph from source data of the enterprise-level demand knowledge graph; Generate graph data based on the entity data and relationship data of the enterprise-level demand knowledge graph and store it in a graph database; The graph data is post-processed based on the graph database to obtain an enterprise-level demand knowledge graph.

2. The method for constructing an enterprise-level demand knowledge graph according to claim 1, characterized in that: The step of extracting entity data and relationship data of the enterprise-level demand knowledge graph from the source data of the enterprise-level demand knowledge graph includes: Extracting first entity data and first relationship data from the information engineering data, wherein the first entity data includes at least one first entity in a system and an engineering and corresponding attribute data, and the first relationship data includes the relationship between the first entities and the corresponding attribute data; Extracting second entity data and second relationship data from the business requirement document, the second entity data including at least one second entity among a function point, a function item, and a function module and corresponding attribute data, the second relationship data including a relationship between the second entities and the corresponding attribute data and a relationship between the second entity and the first entity and the corresponding attribute data; Multiple third entity data and third relationship data are extracted from the expert database data, wherein the third entity data includes at least one third entity among review experts, professional fields and business segments and corresponding attribute data, and the third relationship data includes the relationship between third entities and corresponding attribute data, as well as the relationship between the third entity and the second entity, the first entity and the corresponding attribute data.

3. The method for constructing an enterprise-level demand knowledge graph according to claim 1 is characterized in that: The step of generating graph data based on the entity data and relationship data of the enterprise-level demand knowledge graph and storing the graph data in the graph database includes: Converting the entity data and relationship data of the enterprise-level demand knowledge graph into structured entity data tables and relationship data tables respectively; Configuring a mapping relationship between the structured entity data table and the relationship data table and the graph structure, and converting the structured entity data table and the relationship data table into graph data; The graph data is stored in a graph database.

4. The method for constructing an enterprise-level demand knowledge graph according to claim 3 is characterized in that: The entity data and relationship data of the enterprise-level demand knowledge graph include first entity data and first relationship data, second entity data and second relationship data, and third entity data and third relationship data. The converting the entity data and relationship data of the enterprise-level demand knowledge graph into structured entity data tables and relationship data tables respectively includes: Aggregating the first entity data, the second entity data, and the third entity data to generate a structured entity data table; The first relational data, the second relational data, and the third relational data are aggregated to generate a structured relational data table.

5. The method for constructing an enterprise-level demand knowledge graph according to claim 1, characterized in that: The entity data includes basic attributes and derived attributes of the entity, the attribute values ​​of the basic attributes of the entity contained in the graph data are directly extracted from the source data, and the post-processing of the graph data based on the graph database to obtain the enterprise-level demand knowledge graph includes: According to the entity data and relationship data associated with the derived attributes in the graph data, the attribute value of the derived attributes of the entity is calculated, the entity data associated with the derived attributes includes review experts, and the derived attributes of the entity include the cumulative number of reviews by the review experts.

6. The method for constructing an enterprise-level demand knowledge graph according to any one of claims 1 to 5, characterized in that: The method for constructing the enterprise-level demand knowledge graph also includes: Receive enterprise-level demand knowledge retrieval requests; According to the enterprise-level demand knowledge retrieval request, a search is performed in the enterprise-level demand knowledge graph to obtain an enterprise-level demand knowledge retrieval result.

7. The method for constructing an enterprise-level demand knowledge graph according to claim 6, characterized in that: The enterprise-level demand knowledge retrieval request includes at least one of an engineering information retrieval request, a business demand information retrieval request, and an expert information retrieval request. The engineering information retrieval request is used to retrieve engineering basic data and engineering related data. The business demand information retrieval request is used to retrieve the impact scope of business demand changes. The expert information retrieval request is used to retrieve expert basic data and expert related data.

8. A device for constructing an enterprise-level demand knowledge graph, characterized in that: The device for constructing the enterprise-level demand knowledge graph includes: An acquisition unit, used to acquire source data of an enterprise-level demand knowledge graph, wherein the source data includes information engineering data, business demand documents, and expert database data; An extraction unit, used to extract entity data and relationship data of the enterprise-level demand knowledge graph from the source data of the enterprise-level demand knowledge graph; A generating unit, configured to generate graph data according to the entity data and relationship data of the enterprise-level demand knowledge graph and store the generated graph data in a graph database; A post-processing unit is used to post-process the graph data based on the graph database to obtain an enterprise-level demand knowledge graph.

9. A device comprising: processor; And a memory arranged to store computer executable instructions, which, when executed, cause the processor to execute the method for constructing an enterprise-level demand knowledge graph as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method for constructing an enterprise-level demand knowledge graph as described in any one of claims 1 to 7 is implemented.

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