A method and device for constructing domain ontology based on thesaurus

Abstract

This application proposes a method and device for constructing a domain ontology based on a thesaurus. The method includes the following steps: S1, traversing the thesaurus to extract the associated file set of all entries in the thesaurus; S2, constructing thesaurus vectors: the thesaurus includes N-level thesaurus, traversing the N-level thesaurus, and repeatedly constructing the N-1-level thesaurus vector with the corresponding upper-level thesaurus as the origin; S3, constructing the entry vector: constructing the XY plane vector from the first-level thesaurus to the N-1-level thesaurus; S4, constructing the associated file vector: from the first-level thesaurus, taking the entry vector as the dot product, generating a top-level ontology three-dimensional model determined by the associated file set; and S5, generating a domain ontology model based on the top-level ontology three-dimensional model and the input domain theme. It can solve the problems of duplicate construction and duplicate governance in big data governance, and can form a model layer for the knowledge graph of the ontology library management, with a clear hierarchical structure and reduced redundancy.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and device for constructing a domain ontology based on a thesaurus. Background Art

[0002] Nowadays, in the big data environment, a large number of descriptive knowledge models accumulated in the industry field have problems in use such as isolated access, difficulty in sharing, and weak guidance for big data.

[0003] There are many ways to construct an ontology, such as directly building a domain ontology, building a domain ontology based on an existing taxonomy, or building a domain ontology based on an existing thesaurus. A thesaurus is a collection of words that express concepts within a specific discipline; it is a vocabulary system that uses various methods to display the various semantic connections between the words. The goal of a domain ontology is to capture knowledge in the relevant domain, identify commonly recognized terms within the domain, and clearly define the relationships between these terms using formal models at different levels.

[0004] An ontology is a conceptual model that describes information at a semantic level. By describing concepts and their relationships, it provides a shared, clear, and formalized definition of domain terms. However, ontology construction is still primarily manual, which leads to duplication of effort and management, and makes maintenance difficult.

[0005] In view of this, the present invention proposes a method and device for constructing a domain ontology based on a thesaurus, uniformly mapping various descriptors of various industries to N-dimensional space for vectorization, forming a standard space vector set, and using this set as a method for modeling the pattern layer ontology in the knowledge graph. Summary of the Invention

[0006] In order to solve the technical problems of repeated construction and repeated governance in existing big data governance, the purpose of the present invention is to provide a method and device for constructing a domain ontology based on a thesaurus, which can reduce the degree of redundancy and improve the ability of reuse, sharing and maintainability.

[0007] According to one aspect of the present invention, a method for constructing a domain ontology based on a thesaurus is proposed, the method comprising the following steps:

[0008] S1, traverse the thesaurus and extract the associated file set of all entries in the thesaurus;

[0009] S2. Constructing descriptor vectors: The descriptor table includes N-level descriptors. We traverse N-level descriptors and repeatedly construct N-1-level descriptor vectors with the corresponding previous-level descriptor as the origin.

[0010] S3. Constructing term vector: Constructing XY plane vector from the first-level descriptor to the N-1-level descriptor;

[0011] S4. Constructing associated document vectors: From the first-level descriptors, using the term vectors as dot products, generate a top-level ontology three-dimensional model determined by the associated document set; and

[0012] S5. Generate a domain ontology model based on the top-level ontology three-dimensional model and the input domain topics.

[0013] In a specific embodiment, in step S2, the thesaurus is composed of the thesaurus marked with GB / T13190-1991, and the thesaurus includes N levels of thesaurus. The first level of thesaurus is the name of the thesaurus and the definition content of the same level relationship, the second level to the N-1 level of thesaurus are the collection of thesaurus and definition content of the genus-subordinate relationship, and the N level of thesaurus is the related content entry of the class relationship.

[0014] In a specific embodiment, step S2 also includes traversing the first-level descriptors in the descriptor table, generating a first-level descriptor hot reference table and sorting it in reverse order according to the number of times it has been used, removing duplicate descriptors in the first-level descriptor hot reference table, and generating a discrete X-axis data set, where the coordinates of each descriptor are (X, Y=0, Z=0).

[0015] In a specific embodiment, step S2 also includes traversing all second-level descriptors in the thesaurus and constructing a vector with the corresponding first-level descriptor (X, 0, 0) as the origin; judging whether there are repeated descriptors, if so, the repeated descriptors are not counted in the number of nodes, and generating a vector with the first-level descriptor (X, 0, 0) as the origin and the repeated descriptor vertex as the terminal.

[0016] In a specific embodiment, in step S1, the thesaurus is traversed, the associated file set of all entries in the thesaurus is extracted, a hot reference table of associated files is generated and sorted in reverse order according to the number of times used; the hot reference table of associated files is marked with relative distances according to the number of citations to generate a discrete Z-axis data set.

[0017] In a specific embodiment, in step S5, the domain theme is a keyword or a set of search keywords randomly input by the user.

[0018] In a specific embodiment, in step S5, a domain ontology model is generated based on the top-level ontology three-dimensional model and the input domain subject, which specifically includes the following sub-steps:

[0019] S51, detecting the descriptors included in the domain theme, and obtaining a first associated file based on the detected descriptor topology;

[0020] S52, full-text search to see if the known associated file contains the domain theme, and if so, consider the known associated file as the second associated file;

[0021] S53. Retrieve the term vectors corresponding to the first associated file and the second associated file through the top-level ontology three-dimensional model;

[0022] S54, removing all irrelevant term vectors to generate a domain ontology model; and

[0023] S55. Build a clear domain ontology theme map through three-dimensional roaming.

[0024] In a second aspect, the present application provides a device for constructing a domain ontology based on a thesaurus, the device comprising:

[0025] The associated file acquisition module is used to traverse the thesaurus and extract the associated file sets of all entries in the thesaurus;

[0026] The descriptor vector construction module is used to construct descriptor vectors: the descriptor table includes N-level descriptors, and the N-1-level descriptor vectors are repeatedly constructed with the corresponding previous-level descriptor as the origin.

[0027] The term vector construction module is used to construct the term vector: from the first-level descriptor to the N-1-level descriptor, construct an XY plane vector;

[0028] An associated document vector construction module is used to construct an associated document vector: from the first-level descriptor, using the term vector as the dot product, to generate a top-level ontology three-dimensional model determined by the associated document set; and

[0029] The model generation module is used to generate a domain ontology model based on the top-level ontology three-dimensional model and the input domain topics.

[0030] In a third aspect, the present application provides an electronic device, comprising:

[0031] at least one processor;

[0032] and a memory communicatively coupled to the at least one processor;

[0033] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform any of the methods described above.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, any of the above methods is implemented.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] This application uniformly maps all kinds of descriptors in various industries to N-dimensional space for vectorization, forming a standard set of space vectors, and uses this vector set as a method for ontology modeling in the pattern layer of the knowledge graph. This method can generate public and domain-specific ontology theme models for various fields to solve the problems of duplicate construction and duplicate governance in big data governance, and can also form a pattern layer for the ontology library to manage the knowledge graph. Using this model to manage structured knowledge concept models not only makes the hierarchical structure clear and concise, but also greatly reduces the degree of redundancy, and improves the ability to reuse, share, and maintain. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0038] Figure 1 is a flowchart of a method for constructing a domain ontology based on a thesaurus according to the present application;

[0039] Figure 2 It is a structural diagram of an apparatus for constructing a domain ontology based on a thesaurus according to the present application;

[0040] Figure 3 It is a structural diagram of a computer system suitable for implementing the electronic device of the embodiment of the present application. DETAILED DESCRIPTION

[0041] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0042] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0043] Figure 1 The flowchart of the method for constructing domain ontology based on thesaurus of this application is shown in FIG. Figure 1 , the method comprises the following steps:

[0044] S1. Traverse the thesaurus and extract the associated file set of all entries in the thesaurus.

[0045] In this embodiment, all submitted thesauri are traversed, and the associated file sets of all entries in the thesaurus are extracted to generate a hot reference table of associated files and sort them in reverse order according to the number of times used. The format of the hot reference table of associated files is {URL:number of citations}.

[0046] The associated file heat reference table is annotated with relative distances according to the number of references to generate a discrete Z-axis data set.

[0047] S2. Constructing descriptor vectors: The descriptor table includes N-level descriptors. The N-level descriptors are traversed, and the N-1-level descriptor vectors are repeatedly constructed with the corresponding previous-level descriptors as the origin.

[0048] In this embodiment, the thesaurus is constructed using the GB / T 13190-1991 standard. It includes N levels of descriptors. First-level descriptors are peer-level descriptor names and definitions. Second-level to N-1-level descriptors are genus-level descriptors and definitions. N-level descriptors are related content terms in a categorical relationship. Content terms are composed of terms and a collection of extended system file URLs.

[0049] The format of the N-level descriptor is as follows:

[0050]

[0051]

[0052] In step S2, the first-level descriptors of all submitted thesauri are traversed to generate a first-level descriptor hot citation table, and the table is sorted in reverse order according to the number of times used. The first-level descriptor hot citation table has the format of {descriptor, {defined content merged set}: number of citations}.

[0053] Remove duplicate descriptors in the first-level descriptor hot citation table to generate a discrete X-axis data set, where each descriptor has a coordinate of (X, Y=0, Z=0), where the X value is the sequence N of the first-level descriptor hot citation table.

[0054] Traverse all second-level descriptors in the thesaurus and construct a vector with the corresponding first-level descriptor (X,0,0) as the origin. The vector modulus is 1, and the direction angle Q is 2Pi / N (N is the number of nodes).

[0055] And determine whether there are repeated descriptors. If there are repeated descriptors, they will not be counted in the number of nodes. Generate a vector with the first-level descriptor (X, 0, 0) as the origin and the repeated descriptor vertex as the terminal.

[0056] Next, all the third-level descriptors in the thesaurus are traversed, and a vector is constructed with the corresponding second-level descriptor as the origin. The modulus of the vector is 1, and the direction angle Q is 2Pi / N (N is the number of nodes).

[0057] If the descriptor has appeared before, it will not be counted in the number of nodes, and a vector will be generated with the end point of the secondary descriptor vector as the origin and the repeated descriptor vertex as the terminal.

[0058] And so on, repeatedly construct N-1 level descriptor vectors

[0059] Continue to refer Figure 1 ,The methods of constructing domain ontology based on thesaurus also include:

[0060] S3. Construct term vector: From the first-level descriptor to the N-1-level descriptor, construct an XY plane vector.

[0061] S4. Constructing the associated file vector: Starting from the first-level descriptor, using the term vector as the dot product, a top-level ontology three-dimensional model is generated, determined by the set of associated files. It should be understood that the first-level descriptor is an X vector, and the term vector is the product of two vectors (X, Y) (the angle between the vectors is fixed), also known as the dot product. This dot product generates the Z value, which is the vector in the three-dimensional coordinate system (X, Y, Z), which is also the vector of the associated files. The collection of all associated file vectors is the three-dimensional model of the ontology.

[0062] S5. Generate a domain ontology model based on the top-level ontology three-dimensional model and the input domain topics.

[0063] In a specific embodiment of step S5, the domain theme is a keyword or a set of search keywords randomly input by the user.

[0064] Generate a domain ontology model based on the top-level ontology 3D model and the input domain topics, which specifically includes the following sub-steps:

[0065] S51, detecting the descriptors included in the domain theme, and obtaining a first associated file based on the detected descriptor topology;

[0066] S52, full-text search to see if the known associated file contains the domain theme, and if so, consider the known associated file as the second associated file;

[0067] S53. Retrieve the term vectors corresponding to the first associated file and the second associated file through the top-level ontology three-dimensional model;

[0068] S54, removing all irrelevant term vectors to generate a domain ontology model; and

[0069] S55. Build a clear domain ontology theme map through three-dimensional roaming.

[0070] This application uniformly maps all kinds of descriptors in various industries to N-dimensional space for vectorization, forming a standard set of space vectors, and uses this vector set as a method for ontology modeling in the pattern layer of the knowledge graph. This method can generate public and domain-specific ontology theme models for various fields to solve the problems of duplicate construction and duplicate governance in big data governance, and can also form a pattern layer for the ontology library to manage the knowledge graph. Using this model to manage structured knowledge concept models not only makes the hierarchical structure clear and concise, but also greatly reduces the degree of redundancy, and improves the ability to reuse, share, and maintain.

[0071] Further references Figure 2 As an implementation of the above method, this application provides an embodiment of a device for constructing a domain ontology based on a thesaurus. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices. The device 200 includes the following modules:

[0072] The associated file acquisition module 210 is used to traverse the thesaurus and extract the associated file sets of all entries in the thesaurus;

[0073] The descriptor vector construction module 220 is used to construct a descriptor vector: the descriptor table includes N-level descriptors, traverses N-level descriptors, and repeatedly constructs N-1-level descriptor vectors with the corresponding previous-level descriptor as the origin;

[0074] The term vector construction module 230 is used to construct the term vector: from the first-level descriptor to the N-1-level descriptor, construct an XY plane vector;

[0075] The associated document vector construction module 240 is used to construct an associated document vector: from the first-level descriptors, using the term vectors as dot products, to generate a top-level ontology three-dimensional model determined by the associated document set; and

[0076] The model generation module 250 is used to generate a domain ontology model according to the top-level ontology three-dimensional model and the input domain subject.

[0077] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements any of the above methods.

[0078] Reference below Figure 3 , which shows a structural diagram of a computer system 300 suitable for implementing a terminal device or server of an embodiment of the present application. Figure 3 The terminal device or server shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0079] like Figure 3 As shown, computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage unit 308 into a random access memory (RAM) 303. Various programs and data required for the operation of system 300 are also stored in RAM 303. CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0080] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output section 307 including a liquid crystal display (LCD), a speaker, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0081] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above-mentioned functions defined in the method of the present application are executed. It should be noted that the computer-readable medium described in the present application can be a computer-readable signal medium or a computer-readable medium or any combination of the above two. The computer-readable medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical cable, RF, or any suitable combination thereof.

[0082] Computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0083] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0084] The modules involved in the embodiments of the present application may be implemented in software or in hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor includes a receiving module, an acquisition module, a determination module, a calculation module, and a generation module. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the receiving unit may also be described as a "module that obtains preset, target user configuration information in response to determining that the verification request information includes a user name, a request time, a user signature code, and a client application code."

[0085] As another aspect, the present application also provides a computer-readable medium, which may be included in the server described in the above embodiment; or may exist independently and not be assembled into the server. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the server, the server: receives a verification request message sent by a client of a target user; in response to determining that the verification request message includes a user name, a request time, a user signature code, and a client application code, obtains preset configuration information of the target user, wherein the configuration information includes a preset user password corresponding to the user name; determines whether the verification request message is valid based on the request time, and in response to determining that it is valid, determines whether a preset storage area includes a user signature code; in response to determining that it is not included, stores the user signature code in a preset storage area, and calculates a server application code based on the user password, the request time, and the user signature code; in response to determining that the server application code matches the client application code, generates verification success information for indicating that the verification request is a legitimate request.

[0086] In addition, the computer-readable medium may be included in the terminal device described in the above embodiment, or may exist independently without being incorporated into the terminal device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the terminal device, the terminal device: obtains user information input by the target user, wherein the user information includes a user name and a user password; generates a user signature code representing the target user based on the user information; determines a request time; calculates a client application code based on the user password, the request time, and the user signature code; generates verification request information including the user name, the request time, the user signature code, and the client application code; and sends the verification request information to the server.

[0087] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for constructing a domain ontology based on a thesaurus, characterized in that: The following steps are involved: S1. traverse the thesaurus and extract the associated file set of all entries in the thesaurus; S2. Constructing a descriptor vector: The descriptor table includes N-level descriptors, and the N-1-level descriptor vectors are repeatedly constructed with the corresponding previous-level descriptor as the origin. S3. Constructing term vector: Constructing XY plane vector from the first-level descriptor to the N-1-level descriptor; S4. Constructing an associated document vector: generating a top-level ontology three-dimensional model determined by the associated document set from the first-level descriptors using the term vectors as dot products; and S5. Generate a domain ontology model based on the top-level ontology three-dimensional model and the input domain subject, specifically including the following sub-steps: S51, detecting the descriptive terms contained in the domain subject, and obtaining a first associated file based on the detected descriptive term topology; S52: Full-text search to see if the domain theme is included in the known associated files. If so, the known associated files are considered as the second associated files. S53: Retrieve the term vectors corresponding to the first associated file and the second associated file through the top-level ontology three-dimensional model; S54, removing all irrelevant term vectors to generate a domain ontology model; and S55. Build a clear domain ontology theme map through three-dimensional roaming.

2. The method for constructing a domain ontology based on a thesaurus according to claim 1, characterized in that: In step S2, the thesaurus is composed of the thesaurus marked with GB / T 13190-1991, and the thesaurus includes N levels of thesaurus. The first level of thesaurus is the name of the thesaurus and the definition content of the same level relationship, the second level to the N-1 level of thesaurus are the collection of thesaurus and definition content of the genus-subordinate relationship, and the N level of thesaurus is the related content entry of the class relationship.

3. The method for constructing a domain ontology based on a thesaurus according to claim 1, characterized in that: Step S2 also includes traversing the first-level descriptors in the thesaurus, generating a first-level descriptor hot reference table and removing duplicate descriptors in the first-level descriptor hot reference table in reverse order according to the number of times used, and generating a discrete X-axis data set, where the coordinates of each descriptor are (X, Y=0, Z=0).

4. The method for constructing a domain ontology based on a thesaurus according to claim 1, characterized in that: Step S2 also includes traversing all the second-level descriptors in the thesaurus and constructing a vector with the corresponding first-level descriptor (X, 0, 0) as the origin; judging whether there are repeated descriptors, if so, the repeated descriptors are not counted in the number of nodes, and generating a vector with the first-level descriptor (X, 0, 0) as the origin and the repeated descriptor vertex as the terminal.

5. The method for constructing a domain ontology based on a thesaurus according to claim 1, characterized in that: In step S1, the thesaurus is traversed to extract the associated file set of all entries in the thesaurus, generate a hot reference table of the associated files and sort them in reverse order according to the number of times they have been used; The associated file hot reference table is annotated with relative distances according to the number of references to generate a discrete Z-axis data set.

6. The method for constructing a domain ontology based on a thesaurus according to claim 1, characterized in that: In step S5, the domain theme is a keyword or a set of search keywords randomly input by the user.

7. A device for constructing a domain ontology based on a thesaurus, characterized in that: The device comprises: An associated file acquisition module, configured to traverse the thesaurus and extract associated file sets of all entries in the thesaurus; A descriptor vector construction module is used to construct a descriptor vector: the descriptor table includes N-level descriptors, traverses the N-level descriptors, and repeatedly constructs N-1-level descriptor vectors with the corresponding previous-level descriptor as the origin; The term vector construction module is used to construct the term vector: from the first-level descriptor to the N-1-level descriptor, construct an XY plane vector; An associated document vector construction module is used to construct an associated document vector: from the first-level descriptors, using the term vectors as dot products, to generate a top-level ontology three-dimensional model determined by the associated document set; and The model generation module is used to generate a domain ontology model based on the top-level ontology three-dimensional model and the input domain theme, specifically including: detecting the descriptors contained in the domain theme, and obtaining a first associated file from the detected descriptor topology; full-text searching whether known associated files contain the domain theme, and if so, treating the known associated files as second associated files; retrieving the term vectors corresponding to the first associated file and the second associated file through the top-level ontology three-dimensional model; removing all irrelevant term vectors to generate a domain ontology model; and constructing a clear domain ontology theme map through three-dimensional roaming.

8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, wherein a computer program is stored in the medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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