Term set configuration method and apparatus, electronic device, and computer program product

Abstract

The application discloses a configuration method and device of a term set, electronic equipment and a computer program product. Relates to the field of artificial intelligence, the method comprises the following steps: receiving a term set configuration instruction, obtaining M service terms based on the term set configuration instruction, wherein M is a positive integer; according to the similarity value between each two service terms, the M service terms are fused to obtain N categories, wherein M is greater than or equal to N, and N is a positive integer; inputting the N categories into a term set configuration model to obtain a target term set, wherein the target term set is used to indicate the structured description of the concept model in the preset field. Through the application, the technical problem of low construction efficiency in constructing the term set in the related art is solved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence, and more specifically, to a method, apparatus, electronic device, and computer program product for configuring a set of terms. Background Technology

[0002] With the deep integration of artificial intelligence and knowledge engineering, ontology (also known as a set of terms) has become a core technology for the formal expression of domain knowledge and a key infrastructure supporting semantic understanding, intelligent question answering, knowledge graph construction, and large language model reasoning. By constructing domain ontology, heterogeneous data scattered across different business systems can be semantically integrated, eliminating terminological ambiguity and enabling knowledge sharing, interoperability, and reasoning.

[0003] However, traditional domain ontology construction methods, such as manual operations using graphical user interfaces, produce ontology files that are complex documents that are difficult for machines to directly compare. This easily leads to editing conflicts during collaborative development. When the ontology needs to be updated with business iterations, it is difficult to effectively track changes, compare versions, and merge and rollback, resulting in extremely high maintenance and evolution costs and low industrialization of the construction process. Secondly, related technologies typically store the completed ontology in a single format, such as as a plain text file or directly in a single type of database. This single storage architecture cannot simultaneously meet performance requirements.

[0004] Furthermore, in traditional ontology representations, cross-model references (such as a relationship referencing a source class and a target class) often rely on element names. When business requirements change, necessitating modifications to the name or naming conventions of a concept, all references pointing to that concept will break, triggering a chain reaction and disrupting the consistency of the ontology system. Finally, traditional ontology construction focuses only on the logical relationships between concepts. When natural language is needed to query and locate ontology elements, existing storage solutions can only perform rigid string matching, resulting in low recall and precision in retrieval.

[0005] There is currently no effective solution to the technical problem of low construction efficiency when building term sets in related technologies. Summary of the Invention

[0006] The main objective of this application is to provide a method, apparatus, electronic device, and computer program product for configuring a term set, so as to solve the technical problem of low construction efficiency in the related art when constructing a term set.

[0007] To achieve the above objectives, according to one aspect of this application, a method for configuring a terminology set is provided. The method includes: receiving a terminology set configuration instruction; obtaining M business terms based on the terminology set configuration instruction, where M is a positive integer; performing fusion processing on the M business terms according to the similarity value between every two business terms to obtain N categories, where M is greater than or equal to N, and N is a positive integer; inputting the N categories into a terminology set configuration model, and processing to obtain a target terminology set, wherein the target terminology set is used to indicate a structured description of a conceptual model within a preset domain.

[0008] Optionally, obtaining M business terms based on the terminology set configuration instruction includes: extracting the data source addresses of Y business data sources from the terminology set configuration instruction; extracting business concept terms from the Y business data sources based on the Y data source addresses to obtain Y business concept terms, where Y is a positive integer; obtaining a business document; extracting text from the business document to obtain K text terms, where K is a positive integer; and combining the Y business concept terms and the K text terms to obtain M business terms.

[0009] Optionally, the terminology set configuration model includes a first sub-model, a second sub-model, and a third sub-model. Inputting N categories into the terminology set configuration model and processing them to obtain the target terminology set includes: inputting the N categories into the first sub-model, where the first sub-model sets the hierarchy of the N categories to obtain an initial category file, where the initial category file indicates the hierarchical structure among the N categories; inputting the initial category file into the second sub-model, where the second sub-model adds attributes to each category in the initial category file to obtain a processed category file; and inputting the processed category file into the third sub-model, where the third sub-model configures and applies rules to the processed category file to obtain the target terminology set.

[0010] Optionally, the first sub-model sets up a hierarchy for the N categories to obtain an initial category file, which includes: extracting terminology information associated with each category to obtain N sets of terminology information; determining the semantic inclusion relationship between each category based on the N sets of terminology information to obtain an initial hierarchical structure file; sending the initial hierarchical structure file to the client, wherein the client adjusts the initial hierarchical structure file to obtain an adjusted structure file; upon receiving the adjusted structure file from the client, verifying the adjusted structure file; and if the adjusted structure file passes the verification, determining the adjusted structure file as the initial category file.

[0011] Optionally, the second sub-model adds attributes to each category in the initial category file to obtain the processed category file, which includes: obtaining the synonyms associated with each category in the initial category file to obtain N sets of synonyms; obtaining the terms matching each set of synonyms and setting the terms matching each set of synonyms as the context information of each category; the second sub-model determines the attributes and constraints of the categories based on the context information to obtain the attribute list corresponding to each category in the initial category file; sending the attribute list corresponding to each category in the initial category file to the client, wherein the client adjusts the attribute list corresponding to each category in the initial category file to obtain the adjusted attribute list; upon receiving the adjusted attribute list sent by the client, validating the adjusted attribute list; and if the adjusted attribute list passes the validation, mapping the adjusted attribute list to the initial category file to obtain the processed category file.

[0012] Optionally, the third sub-model configures the application rules for the processed category file to obtain the target term set, which includes: obtaining the descriptive words between every two categories in N categories and obtaining the requirements document; the third sub-model analyzes the descriptive words between every two categories to obtain the logical relationship between every two categories; the third sub-model parses the requirements document to obtain the business rules corresponding to the processed category file; and the processed category file is adjusted based on the business rules and the logical relationship between every two categories to obtain the target term set.

[0013] Optionally, after obtaining the target term set, the method further includes: obtaining a business database and obtaining business data from the business database; and mapping the target term set to the business data in the business database one-to-one.

[0014] To achieve the above objectives, according to another aspect of this application, a terminology set configuration apparatus is provided. The apparatus includes: a receiving unit, configured to receive a terminology set configuration instruction and acquire M business terms based on the instruction, where M is a positive integer; a processing unit, configured to perform fusion processing on the M business terms according to the similarity value between any two business terms to obtain N categories, where M is greater than or equal to N, and N is a positive integer; and an input unit, configured to input the N categories into a terminology set configuration model and process them to obtain a target terminology set, wherein the target terminology set is used to indicate a structured description of a conceptual model within a preset domain.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, a configuration method is provided to control the device where the computer-readable storage medium is located to execute any of the above-mentioned term sets.

[0016] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory storing an executable program, and the processor for running the program, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the configuration method of any of the above-mentioned sets of terms.

[0017] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program, wherein the computer program, when executed by a processor, implements the configuration method of any of the above-described terms set.

[0018] In this embodiment, a term set configuration method is adopted. A term set configuration instruction is received, and M business terms are obtained based on this instruction, where M is a positive integer. The M business terms are then fused according to the similarity value between any two business terms to obtain N categories, where M is greater than or equal to N, and N is a positive integer. These N categories are then input into a term set configuration model to obtain a target term set. This target term set is used to indicate a structured description of a conceptual model within a preset domain. This approach solves the technical problem of low construction efficiency in related technologies. By processing the categories formed by the obtained business terms through the term set configuration model to obtain the target term set, the technical effect of improving the construction efficiency of the term set is achieved. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a configuration method for a set of terms;

[0021] Figure 2 This is a flowchart of a method for configuring a terminology set according to embodiments of this application;

[0022] Figure 3 This is a schematic diagram of a configuration system for a set of terms provided in the embodiments of this application;

[0023] Figure 4 This is a schematic diagram of a hybrid storage module provided according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of a configuration method for an optional term set provided according to an embodiment of this application;

[0025] Figure 6This is a schematic diagram of a configuration device for a terminology set provided according to embodiments of this application;

[0026] Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0027] 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.

[0028] 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.

[0029] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. For example, this system has interfaces with relevant users or organizations to provide users with corresponding operation data for them to choose to agree to or refuse automated decision-making results. Before obtaining relevant information, a request for obtaining the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained after receiving consent from the aforementioned user or organization; if the user chooses to refuse, the expert decision-making process is initiated. Users can view the purpose of data use in real time through authorization decoding and have the right to withdraw authorization or delete data at any time. After the authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0030] It should be noted that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize use or refuse use.

[0031] Example 1

[0032] According to an embodiment of this application, a method embodiment for configuring a set of terms is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a configuration method for a set of terms, such as... Figure 1 As shown, computer terminal 10 (or mobile device) may include one or more ( Figure 1 The processor 102 (which may include, but is not limited to, a microprocessor MCU (Microcontroller Unit) or a programmable gate array (FPGA)) is shown as 102a, 102b, ..., 102n. It also includes a memory 104 for storing data and a transmission device 106 for communication functions. In addition, it may include: a display, an input / output interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a keyboard, a cursor control device, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the term set configuration method in the embodiments of this application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the aforementioned term set configuration method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network interface controller (NIC) and a network interface, which can be connected to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a radio frequency (RF) module, used for wireless communication with the Internet.

[0037] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0038] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for configuring the terminology set shown. Figure 2 This is a flowchart of a method for configuring a terminology set according to embodiments of this application, such as... Figure 2 As shown, the method includes the following steps:

[0039] Step S201: Receive a term set configuration instruction and obtain M business terms based on the term set configuration instruction, where M is a positive integer.

[0040] It should be noted that the terminology set configuration instruction is a trigger signal used to initiate the process of collecting important domain terms. It may include information such as the preferred configuration of data sources and the specific domain targeted by the constructed domain ontology. Business terms refer to words that can represent core business concepts, entities, processes, or states in a specific domain (such as civil aviation, finance, etc.). Business terms are the atomic units of the ontology (i.e., the terminology set). Each term can contain a multilingual name (such as Chinese name and English name), description, a list of synonyms, and source information.

[0041] Upon receiving the terminology set configuration instruction, the specific domain (e.g., "passenger services") and its boundaries (e.g., including core entities and relationships such as passengers, ticket orders, and flights) targeted by the domain ontology being constructed can be clearly defined based on the terminology set configuration instruction. This metadata is recorded as project configuration, and a dedicated repository is initialized as the version control root directory of the ontology. Then, several business terms are initially extracted from multi-source heterogeneous data. It should be noted that business terms can include information manually entered by staff, information extracted from data sources, and information extracted from documents.

[0042] Step S202: Based on the similarity value between every two business terms, perform fusion processing on the M business terms to obtain N categories, where M is greater than or equal to N and N is a positive integer.

[0043] Specifically, after obtaining the business terms, the cosine similarity between the text representations of every two terms (such as names, descriptions, and vectors concatenated with synonyms) can be calculated to determine the closeness of each pair of business terms in the semantic space, i.e., multiple similarity values ​​are obtained. These business terms are then fused based on these similarity values, such as deduplication, merging, and normalization: when the similarity between two terms exceeds a preset threshold, or when an exact match exists, they are determined to be the same concept and merged into a unique conceptual entity. During the merging process, the record with more complete information is retained, and the synonyms and descriptions of the other record are added to the retained record. Simultaneously, definition conflicts are resolved according to preset priorities; that is, if descriptions of the same concept from different sources conflict, the system automatically selects the authoritative description based on priority and stores the lower-priority description in an extended field for reference. Finally, multiple categories are obtained, where a category (or "class") refers to a unique, non-redundant set of concepts obtained after fusion processing. For example, "flight order" and "ticket slip" are merged into one category.

[0044] It should be noted that a globally unique and immutable identifier and YAML skeleton file can also be generated for each merged category, stored in the current branch of the repository, forming a unified initial terminology library for subsequent use in defining classes and hierarchical structures.

[0045] Step S203: Input N categories into the terminology set configuration model and process it to obtain the target terminology set, wherein the target terminology set is used to indicate the structured description of the conceptual model in the preset domain.

[0046] Specifically, after obtaining multiple categories, the categories can be input into the term set configuration model to obtain the target term set. The term set configuration model takes the categories as input, uses the semantic reasoning capabilities of the large language model to analyze the semantic inclusion relationships between categories, and combines human review feedback to generate the final ontology structure definition, that is, to obtain the formal ontology class definition set (target term set). Each target term corresponds to a YAML (YAML in't Markup Language) file in the repository, which contains the complete definition of the class.

[0047] The terminology set configuration method provided in this application embodiment receives a terminology set configuration instruction, obtains M business terms based on the instruction, where M is a positive integer; performs fusion processing on the M business terms according to the similarity value between every two business terms to obtain N categories, where M is greater than or equal to N, and N is a positive integer; inputs the N categories into a terminology set configuration model, and processes them to obtain a target terminology set, where the target terminology set is used to indicate the structured description of the conceptual model within a preset domain. This solves the technical problem of low construction efficiency in related technologies when constructing terminology sets. By processing the categories composed of the obtained business terms through the terminology set configuration model to obtain the target terminology set, the technical effect of improving the construction efficiency of terminology sets is achieved.

[0048] Optionally, in the terminology set configuration method provided in this application embodiment, obtaining M business terms based on the terminology set configuration instruction includes: extracting the data source addresses of Y business data sources from the terminology set configuration instruction; extracting business concept terms from the Y business data sources based on the Y data source addresses to obtain Y business concept terms, where Y is a positive integer; obtaining a business document; extracting text from the business document to obtain K text terms, where K is a positive integer; and combining the Y business concept terms and the K text terms to obtain M business terms.

[0049] It should be noted that the data source address refers to the network connection information used to connect to the external business database, including the address, port number, username, and password. This address allows the system to establish a connection channel with the specific business database. The business data source refers to a registered and configured external relational database or other type of data storage instance, which stores actual business data and its metadata (such as table structure and field comments). To obtain business terminology, semantic analysis of the business data source's metadata (such as table names, field names, and field comments) can be performed using a large language model. This identifies and extracts core business entities or attribute terms, thus obtaining business concept terms. This ensures consistency between ontology terms and existing business data, improving the efficiency and coverage of terminology acquisition.

[0050] Furthermore, unstructured or semi-structured business documents uploaded by users can be obtained, and text extraction can be performed on these documents. This involves using parsing tools to extract the text content from the documents and processing it in chunks according to the context window limitations of a large language model to obtain multiple text terms. This supplements the business semantic information that cannot be obtained solely through database metadata, enriching the diversity of ontology terminology sources. Text terms refer to noun phrases or verb phrases identified from the text content of business documents using natural language processing technology. These terms represent business processes, states, or implicit concepts within the domain. Additionally, terms filled in one by one or imported in batches by staff through a graphical interface can also be obtained, and these terms can then be combined to obtain business terms.

[0051] This embodiment achieves the fusion and access of multi-source heterogeneous data by extracting terms in parallel from different dimensions. It overcomes the problems of incomplete terminology coverage, strong subjectivity, and low efficiency caused by traditional ontology construction relying solely on manual input or a single data source. It improves the breadth and accuracy of terminology collection and lays a solid data foundation for the subsequent construction of a high-quality, fully covered domain ontology.

[0052] Optionally, in the terminology set configuration method provided in the embodiments of this application, the terminology set configuration model includes a first sub-model, a second sub-model, and a third sub-model. Inputting N categories into the terminology set configuration model and processing them to obtain a target terminology set includes: inputting the N categories into the first sub-model, where the first sub-model sets the hierarchy of the N categories to obtain an initial category file, wherein the initial category file is used to indicate the hierarchical structure among the N categories; inputting the initial category file into the second sub-model, where the second sub-model adds attributes to each category in the initial category file to obtain a processed category file; and inputting the processed category file into the third sub-model, where the third sub-model configures application rules for the processed category file to obtain the target terminology set.

[0053] It should be noted that the first sub-model refers to the module that constructs the ontology class inheritance hierarchy, capable of inferring parent-child relationships by analyzing the semantic associations of category names, descriptions, and synonyms. The second sub-model refers to the module responsible for identifying and defining class attributes, capable of recommending specific data attributes by combining data source metadata (such as table fields, types, and comments) and class descriptions. The third sub-model refers to the module responsible for defining business rules and logical constraints, capable of extracting logical rules from business documents or database constraints.

[0054] After configuring the model with the category input term set, the logical class inheritance hierarchy can first be derived from the first sub-model. This determines the inheritance relationship between each category, such as which category is the parent class and which is the child class, thus forming a tree-like classification structure and obtaining the initial category file. The initial category file contains fields such as category name, description, and parent class identifier. Then, the initial category file is input into the second sub-model, which adds attributes to each category in the initial category file, defining the data characteristics (such as name, age, amount, etc.) of each category and adding attribute list fields, including determining the attribute name, data type (such as string, integer), whether it is required, and value constraints, thus obtaining the processed category file.

[0055] Then, the processed category file is input into the third sub-model, which configures the application rules for the processed category file and defines business logic constraints for the category, including determining the triggering conditions of the rule (such as the amount being greater than 5000), the execution action (such as marking the status), and the class to which the rule is attached. This results in a complete set of target terms that includes classes, attributes, and business rules, formed after hierarchical settings, attribute additions, and rule configurations.

[0056] This embodiment achieves efficient ontology construction by setting up a first sub-model, a second sub-model, and a third sub-model to handle category hierarchy, attribute definition, and business rules respectively, which significantly reduces the workload of manual modeling. At the same time, through progressive refinement, it improves the completeness and accuracy of the target term set in terms of logical structure, data definition, and business logic.

[0057] Optionally, in the terminology set configuration method provided in this application embodiment, the first sub-model sets the hierarchy of N categories to obtain an initial category file, which includes: extracting terminology information associated with each category to obtain N sets of terminology information; determining the semantic inclusion relationship between each category based on the N sets of terminology information to obtain an initial hierarchical structure file; sending the initial hierarchical structure file to the client, wherein the client adjusts the initial hierarchical structure file to obtain an adjusted structure file; upon receiving the adjusted structure file sent by the client, verifying the adjusted structure file; and if the adjusted structure file passes the verification, determining the adjusted structure file as the initial category file.

[0058] Specifically, when processed by the first sub-model, the attributes can extract terminology information associated with each category. This terminology information refers to the core metadata that constitutes each category, including the category's unique identifier, English name, Chinese name, description, and a list of synonyms. This provides rich and accurate contextual semantic data for subsequent model inference, enabling the model to deeply understand the connotation of concepts based on detailed information such as the category's name, description, and synonyms. This lays the data foundation for accurately judging the semantic relationships between categories.

[0059] Then, based on the terminology information, the semantic inclusion relationships between each category are determined, resulting in an initial hierarchical structure file. This initial hierarchical structure file is then sent to the client. The semantic inclusion relationship refers to the "Is-A" (belongs to / subtypes) logical relationship between categories; for example, "Regional Flight Orders" is a subclass of "Flight Orders". The client adjusts the initial hierarchical structure file, such as dragging and dropping to adjust levels, modifying parent-child relationships, deleting error suggestions, or adding new classes, resulting in an adjusted structure file. Upon receiving the adjusted structure file from the client, it is validated. For example, a consistency check is performed on the adjusted structure file, primarily checking for logical constraints such as circular inheritance and dangling references. If the adjusted structure file passes the validation, it is designated as the initial category file.

[0060] This embodiment obtains the initial category file through processing by the first sub-model, ensuring the accuracy of the business logic and laying a solid foundation for the detailed definition of subsequent ontology attributes.

[0061] Optionally, in the terminology set configuration method provided in this application embodiment, the second sub-model adds attributes to each category in the initial category file to obtain the processed category file, including: obtaining synonyms associated with each category in the initial category file to obtain N sets of synonyms; obtaining terms matching each set of synonyms and setting the terms matching each set of synonyms as context information for each category; the second sub-model determines the attributes and constraints of the category based on the context information to obtain an attribute list corresponding to each category in the initial category file; sending the attribute list corresponding to each category in the initial category file to the client, wherein the client adjusts the attribute list corresponding to each category in the initial category file to obtain an adjusted attribute list; upon receiving the adjusted attribute list sent by the client, verifying the adjusted attribute list; and if the adjusted attribute list passes the verification, mapping the adjusted attribute list to the initial category file to obtain the processed category file.

[0062] Specifically, when processed by the second sub-model, the synonyms associated with each category in the initial category file are first obtained. These synonyms are used as auxiliary information to help the model more comprehensively understand the actual scope of the category's reference in the business context, providing a broader contextual basis for subsequent accurate attribute identification. Synonyms refer to a list of words that are semantically similar to the category name or refer to the same concept. Then, the terms matching each group of synonyms are obtained. These terms are set as the comprehensive data background corresponding to each category to guide the model in attribute reasoning, i.e., obtaining contextual information. This allows for a more accurate inference of the specific data attributes and constraints that the category should possess. The matching terms refer to other terms or phrases highly related to the synonyms, extracted from a unified terminology library or business documents generated through vector similarity calculation or semantic retrieval.

[0063] Furthermore, the second sub-model determines the attributes and constraints of the categories based on contextual information, obtaining an attribute list for each category in the initial category file. Attributes are data items describing category characteristics, including data attributes (such as name, amount, date) and object attributes (such as references to other categories), requiring definitions of name, data type, whether it is required, default value, and value range. Constraints are rules restricting attribute values, such as enumerated value lists, regular expressions, numerical ranges, and uniqueness requirements. Then, the attribute list for each category in the initial category file is sent to the client. The client adjusts the attribute list for each category in the initial category file, for example, by accepting recommended attributes, modifying attribute names or types, adding or removing constraints, deleting unreasonable recommendations, or manually adding new attributes, thus obtaining the adjusted attribute list. Finally, upon receiving the adjusted attribute list from the client, the adjusted attribute list is validated. For example, it is checked whether the data types are valid, whether the constraint formats are correct, and whether there are any conflicts with the category definitions. If the adjusted attribute list passes the validation, it is mapped to the initial category file. The validated attribute list data is written (or merged) into the properties field of the corresponding initial category file, forming a complete processed category file containing the complete class definition (including inheritance relationships and attribute lists).

[0064] This embodiment utilizes synonyms and matching terms to construct a context, thereby generating a processed category file. This efficiently and accurately completes the detailed definition of class attributes, improving the overall efficiency and quality of ontology construction.

[0065] Optionally, in the terminology set configuration method provided in this application embodiment, the third sub-model configures the application rules for the processed category file to obtain the target terminology set, including: obtaining descriptive words between every two categories in N categories, and obtaining a requirement document; analyzing the descriptive words between every two categories by the third sub-model to obtain the logical relationship between every two categories; parsing the requirement document by the third sub-model to obtain the business rules corresponding to the processed category file; and adjusting the processed category file based on the business rules and the logical relationship between every two categories to obtain the target terminology set.

[0066] Specifically, after obtaining the processed category files, the third sub-model can configure and apply rules to them. First, it can obtain descriptive terms between every two categories and then obtain the requirement documents. The descriptive terms refer to the natural language descriptions (e.g., Chinese and English descriptions) and their synonym lists attached to each category in the processed category files. These texts contain the business meaning and characteristics of the category. The requirement documents refer to unstructured business documents, such as requirement specifications and user manuals, which contain specific business logic, rule constraints, and operational procedures. This provides the third sub-model with two key input sources: first, semantic information extracted from the category's own metadata to understand the potential relationships between categories; and second, rule context extracted from external business documents to identify specific business logic and constraints, providing a sufficient data foundation for subsequent rule generation.

[0067] Furthermore, the third sub-model analyzes the descriptive words between each pair of categories to obtain the logical relationship between each pair of categories. The logical relationship refers to the semantic connection or dependency relationship between classes inferred from the category descriptive words, such as "contains", "triggers", "references", etc. This identifies possible business rule triggering scenarios or object interaction paths, providing contextual basis and relationship guidance for the subsequent generation of specific business rules.

[0068] The third sub-model then parses the requirements document, processing it to obtain the business rules corresponding to the processed category file. These business rules are logical statements of actions or constraints to be taken under specific conditions, such as triggering conditions (e.g., "when the amount is greater than 5000") and execution actions (e.g., "mark as priority processing"). Based on the business rules and the logical relationships between every two categories, the processed category file is adjusted, integrating the generated business rules and logical relationship information into the processed category file. Specifically, this involves adding or updating the `businessRules` field in the category file, recording the associated triggering conditions and actions, enriching the category definition, and thus obtaining the target terminology set.

[0069] This embodiment combines semantic analysis of category descriptors with content parsing of requirements documents, utilizes a third sub-model to identify logical relationships between classes and extract business rules, enriches the semantic expressive power of the ontology, enables the target term set to fully reflect the static structure and dynamic business logic of the domain, and improves the completeness and usability of the ontology.

[0070] Optionally, in the terminology set configuration method provided in the embodiments of this application, after processing to obtain the target terminology set, the method further includes: obtaining a business database and obtaining business data in the business database; and mapping the target terminology set to the business data in the business database one by one.

[0071] It should be noted that after obtaining the target term set, business data can be retrieved from the business database. The business database refers to a relational database or other type of data storage system that has been registered and configured with connection information and contains actual running data within the storage facility. It includes raw records reflecting the current business state, i.e., data rows or objects conforming to specific business logic, such as specific customer information records in a customer table or specific order details in an order table. Then, a one-to-one mapping is established between the target term set and the business data in the business database. This means establishing a definite association between the classes and properties defined in the target term set and the tables and columns in the business database; for example, classes correspond to tables, and properties correspond to columns. This embodiment, by retrieving business data from the business database and mapping the target term set to it, enables the defined ontology to directly drive the querying, cleaning, and instantiation of business data, simplifying the data access process and achieving real-time performance and accuracy of ontology instance data.

[0072] This embodiment also provides a configuration system for a terminology set. Figure 3 This is a schematic diagram of a configuration system based on the terminology set provided in the embodiments of this application, such as... Figure 3 As shown, the system includes a declarative build and version control module and a hybrid storage module. The declarative build and version control module comprises a declarative definition submodule and a collaboration submodule. The declarative definition submodule provides a set of predefined, strongly constrained specifications (YAML Schema) for defining and validating the structure and content of the ontology (i.e., the terminology set). This specification provides a declarative description of all elements constituting the ontology. All ontology elements can include: Classes (defining core domain concepts such as customers and orders), Properties (defining data characteristics (e.g., name, age) or object relationships for classes), Relations (defining semantic connections between classes, such as customer orders), Interfaces (defining a set of abstract properties), Business Rules, and Constraints (declaratively defining logical rules and data validation rules within the domain and attaching them to one or more classes). The collaboration submodule stores all the aforementioned terminology set and related business action code in a repository bound to the project. Users can directly build the ontology through the platform interface. Each operation will be converted into a commit, making the change history clear and traceable.

[0073] It should be noted that each ontology element corresponds to a globally unique and immutable identifier upon creation. Additionally, each element is equipped with multilingual fields, such as English name, Chinese name, English and Chinese descriptions, and a list of synonyms, providing rich linguistic information for subsequent semantic retrieval.

[0074] The hybrid storage module serves as the core storage module, driving a hybrid storage architecture that integrates multiple storage engines. Figure 4 This is a schematic diagram of a hybrid storage module provided according to an embodiment of this application, such as... Figure 4 As shown, the module includes the following core storage components and a synchronization engine. The synchronization engine acts as a coordination module. When the declarative construction and version control module generates a successful ontology (i.e., terminology set), it triggers a synchronization operation. At this time, the synchronization engine can be started. After obtaining the repository, it retrieves all the latest terminology sets of the current branch from the repository, builds a complete ontology model in memory, and then performs the following operations in parallel: clears all nodes and edges of the current branch in the graph database and rebuilds them; deletes and rebuilds the relevant vector space in the vector database and re-inserts the vectors; and updates the corresponding key space in the cache.

[0075] Storage components can include: a repository, a relational database, a graph database, a vector database, and a cache. The repository serves as the sole and final persistent storage location for all ontology definition files and business code. It does not directly serve application queries but acts as a copy of all other storage components, responsible for ensuring the consistency and version traceability of the ontology definition. The relational database stores structured metadata related to the ontology, which may include: project and organizational information, registered external data source connection configurations, fine-grained access control rules, and operation logs. The graph database stores the ontology's topology, which can be obtained when the synchronization engine is triggered to start. The system retrieves the repository, parses all terminology sets within it, creates a node for each class in the terminology set, and an edge for each relation. A vector database supports natural language semantic retrieval. The synchronization engine converts the text fields (Chinese and English names, synonym lists, and Chinese and English descriptions) of each ontology element (class, relation, etc.) into high-dimensional vectors using an embedding model and stores them in a vector space created independently for each field. A cache is used for high-performance, low-latency reading of the latest complete ontology object graph from the currently active branch. It can serialize the parsed terminology set definitions into hash structures, providing high-speed ontology model query capabilities for upper-layer applications.

[0076] The system described above can implement an optional method for configuring a set of terms. Figure 5 This is a schematic diagram of a configuration method for an optional terminology set provided according to an embodiment of this application, such as... Figure 5As shown, the method includes: receiving a term set configuration instruction, which clarifies the specific domain and its boundaries for the domain ontology being constructed, recording this metadata as project configuration, initializing a dedicated repository as the version control root directory for the ontology, and then initially extracting multiple business terms from multi-source heterogeneous data. Next, the cosine similarity between the text representations of each pair of terms can be calculated to determine the closeness of each pair of business terms in the semantic space, i.e., obtaining multiple similarity values. Then, these business terms are fused based on these similarity values ​​to finally obtain multiple categories.

[0077] Furthermore, the first, second, and third sub-models in the terminology set configuration model are used sequentially to determine the categories and their hierarchical structure, obtaining an initial category file. The attributes and constraints of each category are then determined, resulting in a processed category file. Finally, the relationships and business rules corresponding to the processed category file (i.e., the applicable rules) are determined, thus obtaining the target terminology set. This target terminology set is then mixed and stored, distributed, and stored in multiple databases optimized for different tasks.

[0078] After constructing the target term set, the target term set is mapped one-to-one with the business data in the business database. That is, specific business data objects are declared as instances of categories in the target term set. For example, by configuring data source mapping, the data of a certain type of instance is specified to come from a table in an external business database, and field transformation and association are realized through cleaning functions. The declaration or mapping information of the instance is also written into the corresponding category.

[0079] This embodiment processes the categories of acquired business terms through a term set configuration model to obtain a target term set, thereby achieving the technical effect of improving the construction efficiency of the term set.

[0080] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0081] Example 2

[0082] This application also provides a term set configuration device. It should be noted that the term set configuration device of this application can be used to execute the term set configuration method provided in this application. The term set configuration device provided in this application will be described below.

[0083] According to embodiments of this application, an apparatus for implementing the configuration method of the above-described term set is also provided. Figure 6 This is a schematic diagram of a configuration device based on the terminology set provided in the embodiments of this application, such as... Figure 6 As shown, the device includes: a receiving unit 60, a processing unit 61, and an input unit 62.

[0084] The receiving unit 60 is used to receive a term set configuration instruction and obtain M business terms based on the term set configuration instruction, where M is a positive integer;

[0085] Processing unit 61 is used to perform fusion processing on M business terms based on the similarity value between every two business terms to obtain N categories, where M is greater than or equal to N and N is a positive integer;

[0086] Input unit 62 is used to configure the model with N categories of input term sets and process them to obtain target term set, wherein the target term set is used to indicate the structured description of the conceptual model in the preset domain.

[0087] The terminology set configuration device provided in this application embodiment receives a terminology set configuration instruction through a receiving unit 60, and obtains M business terms based on the terminology set configuration instruction, where M is a positive integer; a processing unit 61 performs fusion processing on the M business terms according to the similarity value between every two business terms to obtain N categories, where M is greater than or equal to N, and N is a positive integer; an input unit 62 inputs the N categories into a terminology set configuration model, and processes them to obtain a target terminology set, where the target terminology set is used to indicate the structured description of the conceptual model in a preset domain. This solves the technical problem of low construction efficiency when constructing terminology sets in related technologies. By processing the categories composed of the obtained business terms through the terminology set configuration model to obtain the target terminology set, the technical effect of improving the construction efficiency of the terminology set is achieved.

[0088] Optionally, in the terminology set configuration device provided in this application embodiment, the receiving unit 60 includes: a first extraction module, configured to extract the data source addresses of Y business data sources from the terminology set configuration instruction, and extract business concept terms from the Y business data sources based on the Y data source addresses to obtain Y business concept terms, where Y is a positive integer; a first acquisition module, configured to acquire a business document, perform text extraction on the business document to obtain K text terms, where K is a positive integer; and a combination module, configured to combine the Y business concept terms and the K text terms to obtain M business terms.

[0089] Optionally, in the terminology set configuration device provided in the embodiments of this application, the input unit 62 includes: a first input module, used to input N categories into a first sub-model, and the first sub-model sets the hierarchy of the N categories to obtain an initial category file, wherein the initial category file is used to indicate the hierarchical structure among the N categories; a second input module, used to input the initial category file into a second sub-model, and the second sub-model adds attributes to each category in the initial category file to obtain a processed category file; and a third input module, used to input the processed category file into a third sub-model, and the third sub-model configures application rules for the processed category file to obtain a target terminology set.

[0090] Optionally, in the terminology set configuration device provided in this application embodiment, the input unit 62 includes: a second extraction module, used to extract terminology information associated with each category to obtain N sets of terminology information; a first determination module, used to determine the semantic inclusion relationship between each category based on the N sets of terminology information to obtain an initial hierarchical structure file, and send the initial hierarchical structure file to the client, wherein the client adjusts the initial hierarchical structure file to obtain an adjusted structure file; a first verification module, used to verify the adjusted structure file when the adjusted structure file sent by the client is received; and a second determination module, used to determine the adjusted structure file as the initial category file when the adjusted structure file passes the verification.

[0091] Optionally, in the terminology set configuration device provided in this application embodiment, the input unit 62 includes: a second acquisition module, used to acquire synonyms associated with each category in the initial category file, obtaining N sets of synonyms; a third acquisition module, used to acquire terms matching each set of synonyms, and set the terms matching each set of synonyms as context information for each category; a third determination module, used to determine the attributes and constraints of the category by the second sub-model based on the context information, obtaining an attribute list corresponding to each category in the initial category file; a sending module, used to send the attribute list corresponding to each category in the initial category file to the client, wherein the client adjusts the attribute list corresponding to each category in the initial category file to obtain an adjusted attribute list; a second verification module, used to verify the adjusted attribute list upon receiving the adjusted attribute list sent by the client; and a mapping module, used to map the adjusted attribute list to the initial category file if the adjusted attribute list passes verification, to obtain a processed category file.

[0092] Optionally, in the terminology set configuration device provided in this application embodiment, the input unit 62 includes: a fourth acquisition module, used to acquire descriptive words between every two categories in N categories and acquire a requirement document; an analysis module, used to analyze the descriptive words between every two categories by a third sub-model and process them to obtain the logical relationship between every two categories; a parsing module, used to parse the requirement document by the third sub-model and process it to obtain the business rules corresponding to the processed category file; and an adjustment module, used to adjust the processed category file based on the business rules and the logical relationship between every two categories to obtain the target terminology set.

[0093] Optionally, in the terminology set configuration device provided in the embodiments of this application, the device further includes: an acquisition unit, configured to acquire a business database and acquire business data in the business database after processing to obtain the target terminology set; and a mapping unit, configured to map the target terminology set to the business data in the business database one by one.

[0094] It should be noted that the receiving unit 60, processing unit 61, and input unit 62 mentioned above correspond to steps S201 to S203 in Embodiment 1. The instances and application scenarios implemented by the above units and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above units can also be part of a device and can run in the computer terminal 10 provided in Embodiment 1.

[0095] Example 3

[0096] Embodiments of this application may provide a computer terminal, which may be any computer terminal device in a group of computer terminals. Optionally, in this embodiment, the aforementioned computer terminal may also be replaced with a mobile terminal or an electronic device, etc.

[0097] Optionally, in this embodiment, the computer terminal may be located in at least one of a plurality of network devices in a computer network.

[0098] In this embodiment, the computer terminal described above can execute the program code for the following steps in the terminology set configuration method: receiving a terminology set configuration instruction, obtaining M business terms based on the terminology set configuration instruction, where M is a positive integer; performing fusion processing on the M business terms according to the similarity value between every two business terms to obtain N categories, where M is greater than or equal to N, and N is a positive integer; inputting the N categories into the terminology set configuration model, and processing to obtain a target terminology set, where the target terminology set is used to indicate the structured description of the conceptual model within a preset domain.

[0099] Optionally, the computer terminal described above can execute the program code for the following steps in the terminology set configuration method: extracting the data source addresses of Y business data sources from the terminology set configuration instruction; extracting business concept terms from the Y business data sources based on the Y data source addresses to obtain Y business concept terms, where Y is a positive integer; obtaining a business document; extracting text from the business document to obtain K text terms, where K is a positive integer; and combining the Y business concept terms and the K text terms to obtain M business terms.

[0100] Optionally, the computer terminal described above can execute the program code for the following steps in the terminology set configuration method: inputting N categories into a first sub-model, whereby the first sub-model sets the hierarchy of the N categories to obtain an initial category file, wherein the initial category file is used to indicate the hierarchical structure among the N categories; inputting the initial category file into a second sub-model, whereby the second sub-model adds attributes to each category in the initial category file to obtain a processed category file; and inputting the processed category file into a third sub-model, whereby the third sub-model configures the application rules for the processed category file to obtain the target terminology set.

[0101] Optionally, the computer terminal described above can execute the following steps in the terminology set configuration method: The first sub-model sets the hierarchy of N categories to obtain an initial category file, including: extracting terminology information associated with each category to obtain N sets of terminology information; determining the semantic inclusion relationship between each category based on the N sets of terminology information to obtain an initial hierarchical structure file; sending the initial hierarchical structure file to the client, wherein the client adjusts the initial hierarchical structure file to obtain an adjusted structure file; upon receiving the adjusted structure file from the client, verifying the adjusted structure file; and if the adjusted structure file passes verification, determining the adjusted structure file as the initial category file.

[0102] Optionally, the computer terminal described above can execute the following steps in the terminology set configuration method: obtain the synonyms associated with each category in the initial category file to obtain N sets of synonyms; obtain the terms matching each set of synonyms and set the terms matching each set of synonyms as the context information of each category; the second sub-model determines the attributes and constraints of the category based on the context information to obtain the attribute list corresponding to each category in the initial category file; send the attribute list corresponding to each category in the initial category file to the client, wherein the client adjusts the attribute list corresponding to each category in the initial category file to obtain the adjusted attribute list; upon receiving the adjusted attribute list sent by the client, verify the adjusted attribute list; if the adjusted attribute list passes the verification, map the adjusted attribute list to the initial category file to obtain the processed category file.

[0103] Optionally, the computer terminal described above can execute the program code for the following steps in the terminology set configuration method: obtaining descriptive words between every two categories in N categories, and obtaining a requirements document; analyzing the descriptive words between every two categories by a third sub-model to obtain the logical relationship between every two categories; parsing the requirements document by the third sub-model to obtain the business rules corresponding to the processed category file; adjusting the processed category file based on the business rules and the logical relationship between every two categories to obtain the target terminology set.

[0104] Optionally, the computer terminal described above can execute the program code for the following steps in the method for configuring the term set: obtaining the business database and obtaining business data from the business database; mapping the target term set one-to-one with the business data in the business database.

[0105] Optionally, Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device may include: one or more ( Figure 7 (Only one is shown) processor 702, memory 704, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0106] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the term set configuration method and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the aforementioned term set configuration method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0107] The processor can invoke information and applications stored in memory via a transmission device to execute the steps described above in the configuration method of the aforementioned term set.

[0108] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 7 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 7 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 7 The different configurations shown.

[0109] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0110] Example 4

[0111] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the configuration method of the terminology set provided in Embodiment 1.

[0112] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0113] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: receiving a term set configuration instruction, obtaining M business terms based on the term set configuration instruction, where M is a positive integer; performing fusion processing on the M business terms according to the similarity value between every two business terms to obtain N categories, where M is greater than or equal to N, and N is a positive integer; inputting the N categories into the term set configuration model, and processing to obtain a target term set, where the target term set is used to indicate the structured description of the conceptual model within a preset domain.

[0114] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform the configuration method steps of a set of terms.

[0115] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0116] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0120] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0121] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for configuring a term set, characterized in that, include: Receive a term set configuration instruction, and obtain M business terms based on the term set configuration instruction, where M is a positive integer; The M business terms are fused based on the similarity value between any two business terms to obtain N categories, where M is greater than or equal to N and N is a positive integer; The N categories are input into the terminology set configuration model, and the target terminology set is obtained. The target terminology set is used to indicate the structured description of the conceptual model in the preset domain.

2. The method according to claim 1, characterized in that, Based on the terminology set configuration instructions, M business terms are obtained, including: Y data source addresses of business data sources are extracted from the terminology set configuration instructions. Based on the Y data source addresses, business concept terms are extracted from the Y business data sources to obtain Y business concept terms, where Y is a positive integer. Obtain the business document, extract the text from the business document to obtain K text terms, where K is a positive integer; The Y business concept terms and the K text terms are combined to obtain the M business terms.

3. The method according to claim 1, characterized in that, The terminology set configuration model includes a first sub-model, a second sub-model, and a third sub-model. The N categories are input into the terminology set configuration model, and the resulting target terminology set includes: The N categories are input into the first sub-model, and the first sub-model sets the hierarchy of the N categories to obtain an initial category file, wherein the initial category file is used to indicate the hierarchical structure among the N categories; The initial category file is input into the second sub-model, which adds attributes to each category in the initial category file to obtain the processed category file. The processed category file is input into the third sub-model, which then configures and applies rules to the processed category file to obtain the target term set.

4. The method according to claim 3, characterized in that, The first sub-model is used to hierarchically set the N categories to obtain the initial category file, which includes: Extract the terminology information associated with each category to obtain N sets of terminology information; Based on the N sets of terminology information, the semantic inclusion relationship between each category is determined to obtain an initial hierarchical structure file. The initial hierarchical structure file is then sent to the client, whereby the client adjusts the initial hierarchical structure file to obtain an adjusted structure file. Upon receiving the adjusted structure file sent by the client, the adjusted structure file is verified. If the adjusted structure file passes the verification, the adjusted structure file will be determined as the initial category file.

5. The method according to claim 3, characterized in that, The second sub-model adds attributes to each category in the initial category file, resulting in a processed category file including: Obtain the synonyms associated with each category in the initial category file to get N sets of synonyms; Get the terms that match each set of synonyms, and set the terms that match each set of synonyms as context information for each category; The second sub-model determines the attributes and constraints of the category based on the context information, thereby obtaining the attribute list corresponding to each category in the initial category file; The attribute list corresponding to each category in the initial category file is sent to the client, wherein the client adjusts the attribute list corresponding to each category in the initial category file to obtain the adjusted attribute list; Upon receiving the adjusted attribute list sent by the client, the adjusted attribute list is validated. If the adjusted attribute list passes the verification, the adjusted attribute list is mapped to the initial category file to obtain the processed category file.

6. The method according to claim 3, characterized in that, The target term set is obtained by applying rules to the processed category file configured by the third sub-model, including: Obtain the descriptive terms between every two categories in the N categories, and obtain the requirements document; The third sub-model analyzes the descriptive words between each pair of categories to obtain the logical relationship between each pair of categories; The third sub-model parses the requirement document and processes it to obtain the business rules corresponding to the processed category file; The processed category file is adjusted based on the business rules and the logical relationship between each pair of categories to obtain the target term set.

7. The method according to claim 1, characterized in that, After obtaining the target term set, the method further includes: Obtain the business database and retrieve the business data from the business database; The target term set is mapped one-to-one with the business data in the business database.

8. A device for configuring a set of terms, characterized in that, include: The receiving unit is configured to receive a term set configuration instruction and obtain M business terms based on the term set configuration instruction, where M is a positive integer; The processing unit is used to fuse the M business terms according to the similarity value between every two business terms to obtain N categories, where M is greater than or equal to N and N is a positive integer; The input unit is used to configure the N categories of input term sets into a model and process them to obtain a target term set, wherein the target term set is used to indicate the structured description of the conceptual model within a preset domain.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the configuration method of the set of terms as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the configuration method of the term set as described in any one of claims 1 to 7.

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