Method and device for constructing knowledge graph and computer program product

Abstract

The embodiment of the invention discloses a method and device for constructing a knowledge graph and a computer program product, and the method comprises the steps: carrying out the entity recognition and relation extraction of data in an enterprise-level data fusion platform, and generating basic graph data, the basic atlas data comprises basic entity data and basic relation data between entities; performing data processing and algorithm mining on the basic atlas data to generate derivative atlas data, and performing view splicing on the derivative atlas data to generate application atlas data; and constructing a knowledge graph comprising the basic graph data, the derivative graph data and the application graph data. Through the embodiment of the invention, the problem that data management is disordered when the knowledge graph data is constructed in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a method, apparatus, and computer program product for constructing a knowledge graph. Background Technology

[0002] In recent years, with the rapid development of big data and artificial intelligence technologies, knowledge graphs have been increasingly widely used in the financial industry, becoming an important tool for data management and intelligent decision-making within financial institutions. However, the construction and application of knowledge graphs face the following key challenges: Traditional knowledge graph construction often relies on local data from a single department or application system, lacking the support of an enterprise-level data fusion platform. This limits the accuracy of entity recognition and relationship extraction. Furthermore, inconsistent data standards across systems and differences in the naming and definition of entities and relationships affect the reusability and scalability of the graph. Additionally, the lack of in-depth mining and processing of basic knowledge graph data in related technologies prevents the discovery of implicit relationships and attributes between entities, limiting the richness and depth of derived knowledge graph data.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, and computer program product for constructing a knowledge graph, which at least solves the problem of chaotic data management when constructing knowledge graph data in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for constructing a knowledge graph is provided, comprising: generating basic graph data by performing entity recognition and relationship extraction on data in an enterprise-level data fusion platform, wherein the basic graph data includes basic entity data and basic relationship data between entities; generating derived graph data by performing data processing and algorithm mining on the basic graph data; generating application graph data by performing view assembly on the derived graph data; and constructing a knowledge graph including basic graph data, derived graph data, and application graph data.

[0006] In one exemplary embodiment, generating basic graph data by performing entity recognition and relationship extraction on data in an enterprise-level data fusion platform includes: acquiring data from the enterprise-level data fusion platform; performing entity recognition on the acquired data to obtain basic entity data; performing relationship extraction based on the basic entity data to extract the association relationships between entities and obtain basic relationship data between entities; and converting the basic entity data and basic relationship data into a graph data format to generate basic graph data.

[0007] In one exemplary embodiment, the process of generating derived graph data by processing and mining basic graph data includes: cleaning and standardizing the basic graph data based on preset rules and standards to generate preliminary derived graph data; and mining the preliminary derived graph data using a preset knowledge mining algorithm to identify and calculate non-explicit graph relationship data in the preliminary derived graph data to generate derived graph data.

[0008] In one exemplary embodiment, the preset knowledge mining algorithm includes a natural language processing (NLP) algorithm and a machine learning algorithm.

[0009] In one exemplary embodiment, the data in the enterprise-level data fusion platform includes batch-integrated data from a cloud-based data warehouse and data lake data, wherein the data lake data includes structured data and semi-structured data.

[0010] In one exemplary embodiment, after generating application graph data by assembling views from derived graph data, the method further includes setting view access permissions for the application graph data to enable authorized users or application systems to access the application graph data.

[0011] According to another aspect of the embodiments of this application, an apparatus for constructing a knowledge graph is also provided, comprising: a first generation module, configured to generate basic graph data by performing entity recognition and relationship extraction on data in an enterprise-level data fusion platform, wherein the basic graph data includes basic entity data and basic relationship data between entities; a second generation module, configured to generate derived graph data by performing data processing and algorithm mining on the basic graph data, and generate application graph data by performing view assembly on the derived graph data; and a construction module, configured to construct a knowledge graph including the basic graph data, the derived graph data, and the application graph data.

[0012] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed by a processor.

[0013] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the steps in any of the method embodiments described above.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0015] This application embodiment generates basic graph data by performing entity identification and relationship extraction on data in an enterprise-level data fusion platform. This generates basic graph data, including basic entity data and basic relationship data between entities, ensuring data consistency and standardization, and facilitating subsequent data management and application. Based on the basic graph data, further data processing yields derived graph data. This process reveals complex relationships and patterns hidden within the original data, thereby deepening the understanding and mastery of the data. View assembly technology allows for the dynamic combination and querying of derived graph data according to the needs of specific business scenarios without physically modifying the original data. This makes data application more flexible in different business scenarios, greatly improving the flexibility and efficiency of data services. Attached Figure Description

[0016] Figure 1 This is a schematic diagram illustrating an application scenario of a method for constructing a knowledge graph according to an embodiment of this application;

[0017] Figure 2 This is a flowchart illustrating a method for constructing a knowledge graph according to an embodiment of this application;

[0018] Figure 3 This is a structural block diagram of an apparatus for constructing a knowledge graph according to an embodiment of this application;

[0019] Figure 4 This is a schematic diagram of a knowledge graph system according to an embodiment of this application;

[0020] Figure 5 This is a schematic diagram of the knowledge graph data flow according to an embodiment of this application;

[0021] Figure 6 This is a schematic diagram of the implementation and management mode of knowledge graph application according to the embodiments of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0025] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0026] According to one aspect of the embodiments of this application, a method for constructing a knowledge graph is provided. Optionally, in this embodiment, the above-described method for constructing a knowledge graph may be applied, but is not limited to, to applications such as... Figure 1 The hardware environment shown includes terminal device 102 and server 104. Server 104 can be connected to terminal device 102 via a network and can be used to provide services (e.g., application services, etc.) to terminal device 102 or clients installed on terminal device 102. A database can be set up on server 104 or independently of server 104 to provide data storage services for server 104.

[0027] The aforementioned network may include, but is not limited to, at least one of the following: wired network and wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network (WAN), metropolitan area network (MAN), and local area network (LAN). The aforementioned wireless network may include, but is not limited to, at least one of the following: Wireless Fidelity (WIFI) and Bluetooth. Terminal device 102 may be, but is not limited to, a personal computer (PC), mobile phone, tablet computer, etc. Server 104 may be, but is not limited to, a cloud server, server cluster, or other server types.

[0028] The method for constructing a knowledge graph according to this application embodiment can be executed by server 104, terminal device 102, or jointly by server 104 and terminal device 102. The method for constructing a knowledge graph according to this application embodiment can also be executed by a client installed on terminal device 102.

[0029] Taking the method of constructing a knowledge graph in this embodiment executed by terminal device 102 as an example, Figure 2 This is a flowchart illustrating a method for constructing a knowledge graph according to an embodiment of this application, such as... Figure 2 As shown, the process of this method may include the following steps:

[0030] Step S202: Generate basic graph data by performing entity recognition and relationship extraction on the data in the enterprise-level data fusion platform. The basic graph data includes basic entity data and basic relationship data between entities.

[0031] In step S202, basic graph data is generated by performing entity recognition and relationship extraction on the data in the enterprise-level data fusion platform. This includes: obtaining data from the enterprise-level data fusion platform; performing entity recognition on the obtained data to obtain basic entity data; performing relationship extraction based on the basic entity data to extract the association relationships between entities and obtain basic relationship data between entities; and converting the basic entity data and basic relationship data into a graph data format to generate basic graph data.

[0032] It should be noted that the data in the enterprise-level data fusion platform in this application embodiment includes financial data, and the basic graph data refers to entity and relationship data that has not been mined and is composed of factual relationships. It represents the one-to-one, one-to-many, and many-to-many relationships between conceptual entities and the attributes of the conceptual entities themselves, and is the basic relational information for constructing the knowledge graph.

[0033] In an exemplary embodiment of this application, the data in the enterprise-level data fusion platform includes batch-integrated data from cloud-based data warehouses and data lake data, wherein the data lake data includes structured data and semi-structured data.

[0034] Step S204: Generate derived map data by processing the basic map data and performing algorithm mining; generate application map data by assembling the derived map data into a view.

[0035] In step S204, derived map data is generated by processing the basic map data and performing algorithm mining, including: cleaning and standardizing the basic map data based on preset rules and standards to generate preliminary derived map data; and performing algorithm mining on the preliminary derived map data using a preset knowledge mining algorithm to identify and calculate the non-explicit map relationship data in the preliminary derived map data to generate derived map data.

[0036] In an exemplary embodiment of this application, the preset knowledge mining algorithm includes a natural language processing (NLP) algorithm and a machine learning algorithm.

[0037] It should be noted that the derived graph data is entity and relationship graph data calculated based on the basic graph data through processing and mining algorithms. The system processes, calculates, and mines derived graph data for various application fields as needed in the derived graph data area.

[0038] As an optional implementation, if the basic map data cannot meet the application's processing requirements, each application system needs to submit data requests to supplement the basic map data. Instead of directly integrating data in batches or accessing data from the data lake, data completion should be done uniformly in the basic map data area.

[0039] It should be noted that the application graph data is graph data directly oriented towards specific business scenarios, and the data service is published in the form of data views to provide graph data support to application systems.

[0040] Step S206: Construct a knowledge graph that includes basic graph data, derived graph data, and application graph data.

[0041] In an exemplary embodiment of this application, after generating application graph data by assembling views from derived graph data, the method further includes setting view access permissions for the application graph data to enable authorized users or application systems to access the application graph data.

[0042] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this application.

[0043] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0044] According to another aspect of the embodiments of this application, an apparatus for constructing a knowledge graph is also provided. This apparatus can be used to implement the method for constructing a knowledge graph provided in the above embodiments, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0045] Figure 3 This is a structural block diagram of an apparatus for constructing a knowledge graph according to an embodiment of this application, such as... Figure 3 As shown, the apparatus for constructing a knowledge graph includes: a first generation module, used to generate basic graph data by performing entity recognition and relationship extraction on data in an enterprise-level data fusion platform, wherein the basic graph data includes basic entity data and basic relationship data between entities; a second generation module, used to generate derived graph data by performing data processing and algorithm mining on the basic graph data, and to generate application graph data by performing view assembly on the derived graph data; and a construction module, used to construct a knowledge graph including basic graph data, derived graph data, and application graph data.

[0046] In an exemplary embodiment of this application, the apparatus is further configured to acquire data from an enterprise-level data fusion platform, perform entity recognition on the acquired data to acquire basic entity data; extract relationships based on the basic entity data to extract the association relationships between entities and acquire basic relationship data between entities; and convert the basic entity data and basic relationship data into a graph data format to generate basic graph data.

[0047] In an exemplary embodiment of this application, the apparatus is further configured to perform data cleaning and standardization on the basic atlas data based on preset rules and standards to generate preliminary derived atlas data; and to perform algorithmic mining on the preliminary derived atlas data using a preset knowledge mining algorithm to identify and calculate the non-explicit atlas relationship data in the preliminary derived atlas data to generate derived atlas data.

[0048] In an exemplary embodiment of this application, the apparatus is further configured to set view access permissions for application graph data, so as to enable authorized users or application systems to access the application graph data.

[0049] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0050] The present invention will now be described in conjunction with specific embodiments:

[0051] Figure 4 This is a schematic diagram of a knowledge graph system according to an embodiment of this application, such as... Figure 4 As shown, the knowledge graph system includes integrated graph data and applied graph data. The integrated graph data includes basic graph data and derived graph data.

[0052] Basic graph data includes basic entity data and basic relationship data between entities. Basic entity data includes enterprise data and individual data, while basic relationship data includes data on controlling stake relationships, legal entity relationships, and senior management relationships. Basic graph data refers to entity and relationship data that has not been mined and is composed of factual relationships. It represents one-to-one, one-to-many, and many-to-many relationships between conceptual entities and the inherent attributes of conceptual entities, serving as the foundational relationship information for constructing the graph.

[0053] Derivative graph data is entity and relationship graph data calculated based on basic graph data through processing and mining algorithms. The knowledge graph system uniformly processes, calculates, and mines derivative graph data for various application domains on demand in the derivative graph data area. For applications where the basic graph data cannot meet the requirements for derivative processing, each application system needs to submit a data request to supplement the basic graph data. Instead of directly integrating data in batches or accessing data from a data lake, the knowledge graph system uniformly completes the data in the basic graph data area.

[0054] Application graph data is graph data directly oriented towards specific business scenarios. The knowledge graph system publishes data services in the form of data views to provide graph data support to application systems. The application graph data area does not perform data processing or calculation.

[0055] Figure 5This is a schematic diagram of the knowledge graph data flow according to an embodiment of this application, such as... Figure 5 As shown:

[0056] Batch / real-time integrated data generates basic graph data through data processing. The main source of basic graph data is batch integrated data from cloud data warehouses, which directly extracts and converts relational data into entity and relational data.

[0057] The semi-structured and unstructured data in the data lake are processed to generate basic graph data. Another part of the basic graph data comes from the semi-structured and unstructured data in the data lake, which is extracted and transformed into entity and relational data.

[0058] Basic map data is processed to generate derived map data. Specifically, derived map data is generated based on basic map data through data processing and mining algorithms. In this data area, implicit map relationship data of various application fields are processed, calculated, and mined as needed.

[0059] The integrated map data generates application map data through view authorization. Specifically, the integrated map data provides map data services to the outside world through view authorization, without processing or calculating the data, only assembling and authorizing the view;

[0060] Knowledge graph data provides data services to external parties through data access service capabilities;

[0061] The data application layer obtains and uses data by calling the services provided by the data service layer.

[0062] Figure 6 This is a schematic diagram illustrating the implementation and management model of knowledge graph applications according to embodiments of this application, such as... Figure 6 As shown: Regarding basic graph data, the cloud-based data warehouse - financial knowledge graph follows the bank's goal of creating a unified graph, integrating and accumulating data to gradually form basic graph data covering various business areas.

[0063] Derivative graphs: (R&D status) Cloud-based data warehouse - the laboratory and various application laboratories use artificial intelligence platforms, big data cloud platforms, etc. for joint research; (Operational status) Cloud-based data warehouse - financial knowledge graph is uniformly connected to the R&D status model, providing a unified operating environment for development, production, operation and maintenance management of the mined data results.

[0064] Regarding application graphs: There are two modes of application graph data. Specifically, mode one is the graph data service published by the integrated graph component, including multimodal service modes such as API service, file service, and page service. Mode two is the data authorized to the P9 application component for batch processing into a result list in the cloud data warehouse.

[0065] Regarding the map data service: We provide map data services to application business systems through various methods such as online interfaces, page links, data authorization, and file transfer.

[0066] The following technical effects are achieved through the embodiments of this application:

[0067] A unified standard specification for financial knowledge graphs has been established: a standard for graph data modeling has been established to unify the naming and definition of graph data, avoid duplication of construction, and improve the design, development quality and readability of graph data models; a unified basic logical model for graph data has been established to intuitively describe the logical relationships between all data, facilitating data traceability and management and maintenance; and a full-process implementation process from graph requirements, design, development, testing to online deployment has been formed.

[0068] A unified financial knowledge graph data system has been established: The scope of integrated knowledge graph data has been enriched and improved, consolidating and refining internal knowledge graph foundation and derivative data, expanding the knowledge graph data scope, and enhancing the quality of knowledge graph data; the efficiency of derivative knowledge graph mining has been strengthened, with the R&D team fully utilizing data mining technologies such as artificial intelligence platforms and big data cloud platforms to conduct comprehensive and efficient experimental R&D and deliver operations agilely and uniformly; the value of application knowledge graph services has been expanded and released, relying on the ability to integrate knowledge graph data to conveniently and efficiently extract knowledge graph entities and relational attributes, quickly and agilely forming application subgraphs to empower business applications.

[0069] Unified financial knowledge graph management capabilities: An enterprise-level knowledge graph data warehouse management capability has been built, integrating knowledge management, data analysis, and application empowerment. This enables data management functions covering the integrated graph area and application graph hierarchies, providing real-time statistics on entities, relationships, applications, total data volume, daily increments, and other indicators for clear and intuitive operational monitoring of the graph data area. A unified and comprehensive knowledge graph data management mechanism has been established, improving graph data authorization and approval processes, forming a bank-wide, secure, and efficient graph data access control capability. The implementation process for the bank's financial knowledge graph business needs has been integrated, uniformly connecting and promoting internal knowledge graph requirements from an enterprise-level knowledge graph perspective, and coordinating construction and implementation.

[0070] The financial knowledge graph application service has been unified: knowledge graph data services are released uniformly, comprehensively covering various service types and providing graph data services to application business systems through multiple methods such as online interfaces, page embedding, data authorization, and file transfer. Graph data service information is statistically analyzed periodically to improve the data service management system.

[0071] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0072] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0073] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0074] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0075] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is configured to perform the steps of any of the method embodiments described above via the computer program. In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0076] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0077] According to another aspect of the embodiments of this application, a computer program product is also provided, which includes a computer program / instructions containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions provided in the embodiments of this application. The sequence numbers of the embodiments of this application above are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0078] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0079] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for constructing a knowledge graph, characterized in that, include: Basic graph data is generated by performing entity recognition and relationship extraction on data in an enterprise-level data fusion platform. The basic graph data includes basic entity data and basic relationship data between entities. Derivative map data is generated by processing the basic map data and mining it using algorithms. Then, application map data is generated by assembling the derived map data into a view. Construct a knowledge graph that includes the basic graph data, the derived graph data, and the application graph data.

2. The method according to claim 1, characterized in that, The process of generating basic graph data by performing entity recognition and relationship extraction on data from an enterprise-level data fusion platform includes: Data is obtained from the enterprise-level data fusion platform, and entity recognition is performed on the obtained data to obtain basic entity data; Based on the basic entity data, relationship extraction is performed to extract the association relationships between entities and obtain the basic relationship data between entities. The basic entity data and the basic relationship data are converted into a graph data format to generate the basic graph data.

3. The method according to claim 1, characterized in that, The process of generating derived map data by processing the basic map data and performing algorithmic mining includes: Based on preset rules and standards, the basic atlas data is cleaned and standardized to generate preliminary derived atlas data. The preliminary derived graph data is processed by a preset knowledge mining algorithm to identify and calculate the non-explicit graph relationship data in the preliminary derived graph data, and generate the derived graph data.

4. The method according to claim 3, characterized in that, in, The preset knowledge mining algorithms include natural language processing (NLP) algorithms and machine learning algorithms.

5. The method according to claim 1, characterized in that, in, The data in the enterprise-level data fusion platform includes batch-integrated data from cloud-based data warehouses and data lake data, wherein the data lake data includes structured data and semi-structured data.

6. The method according to claim 1, characterized in that, After generating application map data by assembling the derived map data into views, the process further includes: Set view access permissions for the application graph data so that authorized users or application systems can access the application graph data.

7. An apparatus for constructing a knowledge graph, characterized in that, include: The first generation module is used to generate basic graph data by performing entity recognition and relationship extraction on data in the enterprise-level data fusion platform. The basic graph data includes basic entity data and basic relationship data between entities. The second generation module is used to generate derived map data by processing the basic map data and performing algorithm mining, and to generate application map data by assembling the derived map data into a view. The construction module is used to construct a knowledge graph that includes the basic graph data, the derived graph data, and the application graph data.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

10. 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, it implements the steps of the method according to any one of claims 1 to 6.

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