Domain word library construction method and system for information system

Abstract

The invention relates to a field lexicon construction method and system for an information system, and the method specifically comprises the steps: extracting a first vocabulary from a business field to construct a first lexicon, and extracting a second vocabulary from an information system management system to construct a second lexicon; based on research and development entities in information system research and development, such as research and development personnel, research and development deliverables and research and development organizations, extracting expansion vocabularies to expand the first lexicon; setting a corresponding label for the research and development entity, associating the first vocabulary, the expansion vocabulary and the label, and establishing a relation network; performing word segmentation on the document of the research and development entity by utilizing a word segmentation tool to form a third word bank, and taking the first word bank and the second word bank as basic dictionaries by the word segmentation tool; and classification is performed based on the similarity between the third vocabulary in the third word bank and the first vocabulary and the expanded vocabulary, so that the practicability and the accuracy of the field word bank can be improved, asset retrieval can be optimized, and a data basis is provided for enterprise-level knowledge system construction.

Description

Technical Field

[0001] The present invention relates to the field of information system research and development and knowledge system construction, and specifically proposes a method and system for constructing a domain vocabulary library for information system research and development. Combining the internal research and development process of an enterprise, the documents produced by roles, and the characteristics of the enterprise itself, a domain vocabulary library suitable for enterprise information system research and development is constructed to provide a data basis for knowledge system construction and asset retrieval optimization. Background Art

[0002] With the rapid development of information technology, domain lexicons for industry fields are becoming increasingly abundant, and the openness and sharing of lexicons are becoming increasingly higher. At the same time, technologies such as natural language processing and Chinese word segmentation are becoming increasingly mature, providing strong support for the construction and application of domain lexicons. However, the current open source domain lexicons for industry fields are mostly general industry lexicons, lacking consideration of specific enterprise-specific terminology and context. In the field of enterprise information system R&D, general domain lexicons often cannot effectively support the knowledge mining, R&D asset retrieval, and asset data analysis of the enterprise's internal R&D document library.

[0003] Specifically, the general domain vocabulary may not contain the enterprise-specific business terms, technical terms, and organizational structure-related terms, which results in the inability to accurately match the enterprise's internal professional terms when performing document retrieval, knowledge mining, and other operations, thereby affecting retrieval efficiency and accuracy. In addition, since the general vocabulary lacks understanding of the enterprise's specific context, it may also lead to information omissions or misunderstandings in the process of building a knowledge system, which in turn affects the enterprise's knowledge management and innovation capabilities.

[0004] In order to solve the above problems, the present invention proposes a new method for constructing a domain vocabulary library, which is specifically aimed at the field of information system research and development, and builds a more accurate and practical domain vocabulary library by combining the characteristics and actual needs of the enterprise. The present invention extracts and integrates vocabulary from multiple dimensions such as business fields, organizational structures, and system construction to form a domain vocabulary library suitable for enterprise information system research and development, thereby providing a solid data foundation for subsequent knowledge system construction and asset retrieval optimization. Summary of the invention

[0005] In order to solve the deficiencies of the prior art, the present invention proposes a method and system for constructing a domain vocabulary for an information system.

[0006] To achieve the above objectives, the technical solutions adopted by the present invention include:

[0007] A method for constructing a domain vocabulary for an information system, characterized by comprising the following steps:

[0008] S1. construct a first vocabulary, wherein the first vocabulary comprises a first vocabulary extracted from a business field;

[0009] S2. construct a second vocabulary, wherein the second vocabulary comprises a second vocabulary extracted from the information system management system;

[0010] S3. Expanding the first vocabulary based on an expanded vocabulary, wherein the expanded vocabulary is extracted based on R&D entities in information system R&D, and the R&D entities are R&D personnel, R&D deliverables, and R&D organizations;

[0011] S4. Based on the information system development process, set a corresponding label for the development entity, and associate the first vocabulary and the expanded vocabulary in the first vocabulary with the label to establish a relationship network;

[0012] S5. Based on the relationship network, a word segmentation tool is used to segment the documents of the R&D entity to form a third word library, wherein the word segmentation tool uses the first word library and the second word library as basic dictionaries;

[0013] S6. Calculate the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary, and classify the third vocabulary accordingly.

[0014] Furthermore, the step S1 of constructing the first vocabulary further includes:

[0015] S11. Extract keywords related to business processes from business domain documents;

[0016] S12. Classify the extracted keywords according to the business process;

[0017] S13. Store the classified keywords in the first vocabulary.

[0018] Furthermore, the step S2 of constructing the second vocabulary further includes:

[0019] S21. Extract keywords related to the management system from the information system management system documents;

[0020] S22. Classify the extracted keywords according to the level and function of the management system;

[0021] S23. Store the classified keywords in the second vocabulary.

[0022] Furthermore, the step S3 of expanding the first vocabulary based on the expanded vocabulary further includes:

[0023] S31. Determine the relevant terminology based on the roles and responsibilities of the R&D entity;

[0024] S32. Extract vocabulary related to professional terminology from the outputs of R&D entities;

[0025] S33. Compare the extracted words with the words in the first word library, remove duplicates and expand them to the first word library.

[0026] Furthermore, in step S4, setting a corresponding label for the research and development entity based on the information system research and development process further includes:

[0027] S41. Define the stages in the R&D process;

[0028] S42. Determine the corresponding R&D entity for each stage;

[0029] S43. Assign a corresponding label to each R&D entity, where the label indicates the role or output type of the R&D entity at that stage.

[0030] Furthermore, the step S5 of segmenting the R&D entity document using a segmentation tool based on the relationship network further includes:

[0031] S51. Preprocess the document using natural language processing technology;

[0032] S52. Use the first word library and the second word library as basic dictionaries to segment the document;

[0033] S53. Match the word segmentation results with the words in the relationship network to form a third word library.

[0034] Furthermore, the step S6 of calculating similarity and classifying based on the third vocabulary in the third vocabulary library further includes:

[0035] S61. Using a similarity calculation algorithm, such as cosine similarity or Jaccard similarity, calculate the similarity between the third vocabulary and the first vocabulary and the expanded vocabulary;

[0036] S62. According to the calculated similarity, classify the third vocabulary into the category with the highest similarity to the first vocabulary or the expanded vocabulary.

[0037] In addition, the present invention also relates to a domain vocabulary building system for an information system, characterized in that it includes:

[0038] A first vocabulary building module, used to extract first words from the business field and build a first vocabulary;

[0039] A second word library construction module, used to extract second words from the information system management system and construct a second word library;

[0040] An expanded vocabulary extraction module, used to extract expanded vocabulary and expand the first vocabulary based on R&D entities in information system R&D, wherein the R&D entities include R&D personnel, R&D deliverables, and R&D organizations;

[0041] a label setting module, used to set a corresponding label for the research and development entity, and associate the first vocabulary and the expanded vocabulary in the first vocabulary library with the label to establish a relationship network;

[0042] A third word library construction module is used to call a word segmentation tool based on the relationship network to segment the documents of the R&D entity to form a third word library, wherein the word segmentation tool uses the first word library and the second word library as basic dictionaries;

[0043] The vocabulary classification module is used to calculate the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary, and classify the third vocabulary according to the similarity.

[0044] In addition, the present invention also relates to an electronic device, characterized in that it comprises a processor and a memory;

[0045] The memory is used to store operation instructions;

[0046] The processor is used to execute the above method by calling the operation instruction.

[0047] In addition, the present invention also relates to a computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and the computer program implements the above method when executed by a processor.

[0048] The present invention discloses a method and system for constructing a domain vocabulary for information systems, which aims to combine enterprise characteristics and R&D processes to construct a domain vocabulary suitable for information system R&D. The method comprises the following steps: extracting a first vocabulary from a business field to construct a first vocabulary, and extracting a second vocabulary from an information system management system to construct a second vocabulary; extracting extended vocabulary to expand the first vocabulary based on R&D entities in information system R&D, such as R&D personnel, R&D deliverables, and R&D organizations; setting corresponding labels for R&D entities, and associating the first vocabulary and extended vocabulary with the labels to establish a relationship network; using a word segmentation tool to segment the documents of the R&D entity to form a third vocabulary, and the word segmentation tool uses the first vocabulary and the second vocabulary as the basic dictionary; and classifying based on the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the extended vocabulary. Therefore, the technical solution can improve the practicality and accuracy of the domain vocabulary, optimize asset retrieval, and provide a data basis for the construction of an enterprise-level knowledge system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of a method for constructing a domain vocabulary for an information system provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the structure of a domain vocabulary building system for an information system provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present invention.

[0053] Those skilled in the art can understand that, unless otherwise stated, the singular forms "one", "an", "said" and "the" used herein may also include plural forms, and the "first", "second", etc., are only used to distinguish and define objects for the purpose of introducing the scheme clearly, and do not limit the objects themselves. Of course, the objects defined by "first" and "second" may be the same terminal, device, user, etc., or the same type of terminal, device and user. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0054] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0055] like Figure 1 As shown, a method for constructing a domain vocabulary for an information system is characterized by comprising:

[0056] S1. construct a first vocabulary, wherein the first vocabulary comprises a first vocabulary extracted from a business field;

[0057] Specifically, keywords related to the business process are extracted from the bank business domain documents. For example, for the deposit business process, keywords such as "deposit", "account", "interest rate", etc. are extracted. Then, these keywords are classified according to the business process, for example, "deposit" is classified into the deposit business process. Finally, the classified keywords are stored in the first word library to form a basic vocabulary library of the business domain.

[0058] Extract vocabulary based on business fields to ensure the professionalism and pertinence of the vocabulary, and provide high-quality basic data for subsequent knowledge mining and asset retrieval.

[0059] S2. construct a second vocabulary, wherein the second vocabulary comprises a second vocabulary extracted from the information system management system;

[0060] Specifically, keywords related to the management system are extracted from the bank's information system management system documents. For example, keywords related to IT service management such as "ITIL", "service desk", "incident management", etc. are extracted. These keywords are classified according to the level and function of the management system, for example, "ITIL" is classified into the IT service management framework. Finally, the classified keywords are stored in the second vocabulary to form the basic vocabulary of the management system.

[0061] Constructing the second vocabulary can enhance the professionalism of information system management, enable relevant knowledge of the management system to be effectively retrieved and utilized, and improve the management efficiency of the information system.

[0062] S3. Expanding the first vocabulary based on an expanded vocabulary, wherein the expanded vocabulary is extracted based on R&D entities in information system R&D, and the R&D entities are R&D personnel, R&D deliverables, and R&D organizations;

[0063] Specifically, according to the roles and responsibilities of the bank's R&D team, determine the relevant professional terms. For example, for R&D personnel, determine professional terms such as "API", "database", "front-end development", etc. Then, extract words related to these professional terms from the output of the R&D personnel, such as extracting "Java", "SQL" and other words from the code base. Finally, compare these extracted words with the words in the first word library, remove duplicates and expand them into the first word library.

[0064] By expanding the first vocabulary, the vocabulary can be made more comprehensive and rich, covering enterprise-specific technical terms and industry terms, thereby improving the accuracy of document processing and knowledge retrieval.

[0065] S4. Based on the information system development process, set a corresponding label for the development entity, and associate the first vocabulary and the expanded vocabulary in the first vocabulary with the label to establish a relationship network;

[0066] Specifically, define the various stages in the development process of banking information systems, such as requirements analysis, system design, coding implementation, testing and verification, etc.

[0067] Determine the corresponding R&D entities for each stage. For example, in the requirements analysis stage, the R&D entities include business analysts, project managers, and requirements engineers; in the system design stage, the R&D entities include system architects and design engineers; in the coding implementation stage, the R&D entities include developers and code reviewers; in the testing and verification stage, the R&D entities include test engineers and quality assurance specialists;

[0068] Assign a corresponding label to each R&D entity. The label indicates the role or output type of the R&D entity at that stage. For example, a business analyst is assigned the label "Requirements Analysis: Business Analyst"; a system architect is assigned the label "System Design: Architect"; a developer is assigned the label "Coding Implementation: Developer"; and a test engineer is assigned the label "Test Verification: Test Engineer".

[0069] A relationship network is established to associate the first terms in the first vocabulary with the expanded terms through these tags. For example, if "deposit" is a first term and "online deposit" is an expanded term, these two terms can be associated with the business analyst's tag "Requirement Analysis: Business Analyst" because these terms are related to the deposit business process, and the business analyst is responsible for defining this process during the requirement analysis phase.

[0070] By setting labels for R&D entities, the responsibilities and contributions of each role in the R&D process can be clearly identified, and the associations between words and the collaboration and process dependencies between R&D entities can be reflected through the relationship network. The relationship network provides rich contextual information for subsequent word segmentation and vocabulary classification, thereby improving the accuracy and practicality of the domain vocabulary.

[0071] S5. Based on the relationship network, a word segmentation tool is used to segment the documents of the R&D entity to form a third word library, wherein the word segmentation tool uses the first word library and the second word library as basic dictionaries;

[0072] Specifically, natural language processing technology is used to pre-process the documents of the bank's R&D team, such as removing stop words and part-of-speech tagging.

[0073] The first word library and the second word library are used as the basic dictionary to segment the document, and the segmentation results are matched with the words in the relationship network to form a third word library. For example, if the document mentions "deposit", it is matched with the word "deposit" in the first word library, and the relevant information is stored in the third word library.

[0074] The application of word segmentation tools can improve the efficiency and accuracy of document analysis, so that key content in information system R&D documents can be quickly extracted and utilized.

[0075] S6. Calculate the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary, and classify the third vocabulary accordingly.

[0076] Specifically, a similarity calculation algorithm, such as cosine similarity or Jaccard similarity, is used to calculate the similarity between the vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary. For example, the similarity between the vocabulary "online deposit" and "deposit" is calculated, and according to the calculated similarity, "online deposit" is classified into the category with the highest similarity to "deposit".

[0077] Adopting the scheme provided by this application, extract the first vocabulary from the business field to construct the first vocabulary, and extract the second vocabulary from the information system management system to construct the second vocabulary; based on the R&D entities in the information system R&D, such as R&D personnel, R&D deliverables, and R&D organizations, extract and expand the vocabulary to expand the first vocabulary; set corresponding labels for the R&D entities, and associate the first vocabulary and the expanded vocabulary with the labels to establish a relationship network; use the word segmentation tool to segment the documents of the R&D entity to form a third vocabulary, and the word segmentation tool uses the first vocabulary and the second vocabulary as the basic dictionary; classify based on the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary. Therefore, this technical solution can improve the practicality and accuracy of the domain vocabulary, optimize asset retrieval, and provide a data foundation for the construction of an enterprise-level knowledge system.

[0078] In addition, another aspect also relates to a domain word library construction system for information systems, the structure of which is as follows: Figure 2 As shown, including:

[0079] A first word library construction module 201, used to extract first words from the business field and construct a first word library;

[0080] Specifically, keywords related to business processes are extracted from business domain documents;

[0081] Categorize the extracted keywords according to the business process;

[0082] The classified keywords are stored in the first vocabulary.

[0083] A second word library construction module 202, used to extract second words from the information system management system and construct a second word library;

[0084] Specifically, keywords related to the management system are extracted from the information system management system documents;

[0085] The extracted keywords were classified according to the level and function of the management system;

[0086] The classified keywords are stored in the second vocabulary.

[0087] An expanded vocabulary extraction module 203, used to extract expanded vocabulary and expand the first vocabulary based on R&D entities in information system R&D, wherein the R&D entities include R&D personnel, R&D deliverables, and R&D organizations;

[0088] Specifically, according to the roles and responsibilities of the R&D entity, determine the professional terms related to it;

[0089] Extracting vocabulary related to professional terminology from the outputs of R&D entities;

[0090] The extracted words are compared with the words in the first word library, and the words are expanded to the first word library after removing duplicates.

[0091] A label setting module 204, configured to set a corresponding label for the research and development entity, and associate the first vocabulary and the expanded vocabulary in the first vocabulary library with the label to establish a relationship network;

[0092] Specifically, define the various stages in the R&D process;

[0093] Identify the corresponding R&D entity for each stage;

[0094] A corresponding label is assigned to each R&D entity, and the label indicates the role or output type of the R&D entity at that stage.

[0095] A third word library construction module 205 is used to call a word segmentation tool based on the relationship network to segment the documents of the R&D entity to form a third word library, wherein the word segmentation tool uses the first word library and the second word library as basic dictionaries;

[0096] Specifically, natural language processing technology is used to pre-process the documents;

[0097] Using the first word library and the second word library as basic dictionaries, segmenting the document;

[0098] The word segmentation results are matched with the words in the relational network to form a third word library.

[0099] The vocabulary classification module 206 is used to calculate the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary, and classify the third vocabulary according to the similarity.

[0100] Specifically, a similarity calculation algorithm, such as cosine similarity or Jaccard similarity, is used to calculate the similarity between the third vocabulary and the first vocabulary and the expanded vocabulary;

[0101] According to the calculated similarity, the third vocabulary is classified into a category having the highest similarity with the first vocabulary or the expanded vocabulary.

[0102] By using this system, the above method can be executed and the corresponding technical effects can be achieved.

[0103] An embodiment of the present invention further provides an electronic device for executing the above method, as an implementation device of the method, comprising a processor and a memory;

[0104] A memory, used for storing operation instructions;

[0105] The processor is used to execute a method for constructing a domain vocabulary for an information system provided in any embodiment of the present application by calling an operation instruction.

[0106] As an example, Figure 3 A schematic diagram of the structure of an electronic device applicable to an embodiment of the present application is shown, and the electronic device 300 includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one. It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the specific structure of the electronic device 300. In other embodiments of the present application, the electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. Optionally, the electronic device may also include a display screen 305 for displaying images, or receiving user operation instructions when necessary.

[0107] The processor 301 is applied in the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 304 may include a receiver and a transmitter. The transceiver 304 is applied in the embodiment of the present application to implement the function of the electronic device of the embodiment of the present application communicating with other devices when executed.

[0108] Processor 301 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0109] The processor 301 may also include one or more processing units, for example: the processor 301 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a memory, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and / or a neural network processor (Neural-network Processing Unit, NPU), etc. Different processing units can be independent devices or integrated into one or more processors. Among them, the controller can be the nerve center and command center of the electronic device 300. The controller can generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. A memory can also be set in the processor 301 for storing instructions and data. In some embodiments, the memory in the processor 301 is a cache memory. The memory can save instructions or data that the processor 301 has just used or circulated.

[0110] The processor 301 can run the domain vocabulary construction method for the information system provided in the embodiment of the present application. The processor 301 may include different devices. For example, when the CPU and GPU are integrated, the CPU and the GPU can cooperate to execute the domain vocabulary construction method for the information system provided in the embodiment of the present application. Part of the algorithm is executed by the CPU, and the other part of the algorithm is executed by the GPU to obtain faster processing efficiency.

[0111] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0112] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or may include a high-speed random access memory, or may include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0113] Optionally, the memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the domain word library construction method for information system provided in any embodiment of the present application.

[0114] The memory 303 can be used to store computer executable program codes, which include instructions. The processor 301 executes various functional applications and data processing of the electronic device 300 by running the instructions stored in the memory 303. The memory 303 may include a program storage area and a data storage area. Among them, the program storage area may store the code of the operating system, application program, etc. The data storage area may store data created during the use of the electronic device 300 (such as images, videos, etc. collected by the camera application), etc.

[0115] The memory 303 may also store one or more computer programs corresponding to the domain vocabulary building method for an information system provided in an embodiment of the present application. The one or more computer programs are stored in the above-mentioned memory 303 and are configured to be executed by the one or more processors 301. The one or more computer programs include instructions, and the above-mentioned instructions can be used to execute each step in the above-mentioned corresponding embodiment.

[0116] Of course, the code of the method for constructing a domain vocabulary for an information system provided in the embodiment of the present application can also be stored in an external memory.

[0117] The display screen 305 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 300 may include 1 or N display screens 305, where N is a positive integer greater than 1. The display screen 305 can be used to display information input by a user or information provided to a user and various graphical user interfaces (GUI). For example, the display screen 305 can display photos, videos, web pages, or files.

[0118] The electronic device provided in the embodiment of the present application is applicable to any embodiment of the above method. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0119] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the method in the above embodiment, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps in the method in the above embodiment are implemented.

[0120] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0122] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0124] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for constructing a domain vocabulary for an information system, characterized in that: include: S1. Constructing a first vocabulary, wherein the first vocabulary comprises a first vocabulary extracted from a business field; S2. construct a second vocabulary, wherein the second vocabulary comprises a second vocabulary extracted from the information system management system; S3. Expanding the first vocabulary based on an expanded vocabulary, wherein the expanded vocabulary is extracted based on R&D entities in information system R&D, and the R&D entities are R&D personnel, R&D deliverables, and R&D organizations; S4. Based on the information system development process, set a corresponding label for the development entity, and associate the first vocabulary and the expanded vocabulary in the first vocabulary with the label to establish a relationship network; S5. Based on the relationship network, a word segmentation tool is used to segment the document of the R&D entity to form a third word library, wherein the word segmentation tool uses the first word library and the second word library as a basic dictionary; S6. Calculate the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary, and classify the third vocabulary accordingly.

2. The method according to claim 1, characterized in that The step S1 of constructing the first vocabulary further comprises: S11. Extract keywords related to business processes from business domain documents; S12. Classify the extracted keywords according to the business process; S13. Store the classified keywords in the first vocabulary.

3. The method according to claim 1, characterized in that The step S2 of constructing the second vocabulary further comprises: S21. Extract key words related to the management system from the information system management system documents; S22. Classify the extracted keywords according to the level and function of the management system; S23. Store the classified keywords in the second vocabulary.

4. The method according to claim 1, characterized in that The step S3 of expanding the first vocabulary based on the expanded vocabulary further comprises: S31. Determine the relevant terminology based on the roles and responsibilities of the R&D entity; S32. Extract vocabulary related to professional terminology from the outputs of R&D entities; S33. Compare the extracted words with the words in the first word library, remove duplicates and expand them to the first word library.

5. The method according to claim 1, characterized in that In step S4, setting a corresponding label for the R&D entity based on the information system R&D process further includes: S41. Define the stages in the R&D process; S42. Determine the corresponding R&D entity for each stage; S43. Assign a corresponding label to each R&D entity, where the label indicates the role or output type of the R&D entity at that stage.

6. The method according to claim 1, characterized in that The step S5 of segmenting the R&D entity document using a segmentation tool based on the relationship network further includes: S51. Preprocess the document using natural language processing technology; S52. Use the first word library and the second word library as basic dictionaries to segment the document; S53. Match the word segmentation results with the words in the relationship network to form a third word library.

7. The method according to claim 1, characterized in that The step S6 of calculating similarity and classifying based on the third vocabulary in the third vocabulary library further includes: S61. Using a similarity calculation algorithm, such as cosine similarity or Jaccard similarity, calculate the similarity between the third vocabulary and the first vocabulary and the expanded vocabulary; S62. According to the calculated similarity, classify the third vocabulary into the category with the highest similarity to the first vocabulary or the expanded vocabulary.

8. A domain vocabulary building system for information systems, characterized in that: include: A first vocabulary building module, used to extract first words from the business field and build a first vocabulary; A second word library construction module, used to extract second words from the information system management system and construct a second word library; An expanded vocabulary extraction module, used to extract expanded vocabulary and expand the first vocabulary based on R&D entities in information system R&D, wherein the R&D entities include R&D personnel, R&D deliverables, and R&D organizations; a label setting module, used to set a corresponding label for the research and development entity, and associate the first vocabulary and the expanded vocabulary in the first vocabulary library with the label to establish a relationship network; A third word library construction module is used to call a word segmentation tool based on the relationship network to segment the documents of the R&D entity to form a third word library, wherein the word segmentation tool uses the first word library and the second word library as basic dictionaries; The vocabulary classification module is used to calculate the similarity between the third vocabulary in the third vocabulary and the first vocabulary and the expanded vocabulary, and classify the third vocabulary according to the similarity.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store operation instructions; The processor is used to execute the method according to any one of claims 1 to 7 by calling the operation instruction.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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