CN115080762A - Examination knowledge graph relation establishing method and system - Google Patents
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Abstract
The invention provides a method and a system for establishing examination knowledge graph relation, wherein the method comprises the following steps: acquiring a knowledge graph data source; extracting the association relation between the entities from the related corpora of the knowledge graph data source; integrating knowledge in a plurality of knowledge bases according to the incidence relation to form an integral knowledge base; and carrying out knowledge processing on the whole knowledge base. The method solves the problem that data in the field is dispersed in a plurality of systems, the data is various and complex, isolated and the single data is not high in value, and builds visual and structured knowledge, so that learners can clearly determine the targets, progress, paths and feedback of the learners, and the learning efficiency is improved.
Description
Technical Field
The invention relates to the field of on-line examination training, in particular to an examination knowledge graph relation establishing method and system.
Background
The on-line examination training field for vocational education solves the problems of heavy economic burden and poor learning effect of staff on skill examination training through clear knowledge maps, intelligent learning arrangement and flexible payment on demand.
How to let the employees easily and efficiently master knowledge and skills and obtain professional certification becomes a technical problem to be solved urgently in the prior art.
Disclosure of Invention
In view of the above, the present invention has been made to provide an examination knowledge graph relationship establishing method and system that overcomes or at least partially solves the above problems.
According to one aspect of the invention, the examination knowledge graph relation establishing method comprises the following steps:
acquiring a knowledge graph data source;
extracting the association relation between the entities from the related corpora of the knowledge graph data source;
integrating knowledge in a plurality of knowledge bases according to the incidence relation to form an integral knowledge base;
and carrying out knowledge processing on the whole knowledge base.
Optionally, the knowledge processing on the overall knowledge base specifically includes:
and after quality evaluation, the qualified part is incorporated into a knowledge system to ensure the quality of the knowledge base.
Optionally, the acquiring a data source of a knowledge graph specifically includes:
the knowledge graph data source is divided into two types according to source channels, and the two types comprise: one is the data of the business itself, which is contained in an internal database table and stored in a structured way, and is non-public or semi-public data; the other is data which is published and captured on the network, and the data usually exists in the form of web pages and is unstructured data;
the knowledge graph data source is divided into structured data, semi-structured data and unstructured data according to different data structures;
and processing by adopting a corresponding method according to different data types.
Optionally, the extracting of the association relationship between the entities from the related corpora of the knowledge-graph data source specifically includes:
acquiring text corpora from related corpora of the knowledge graph data source;
the text corpus is subjected to entity extraction to obtain a series of discrete named entity nodes;
and extracting the association relation among the entities from the related linguistic data, and associating a plurality of entities or concepts to form a reticular knowledge structure.
Optionally, the integrating knowledge in the multiple knowledge bases according to the association relationship to form an overall knowledge base specifically includes:
integrating knowledge in a plurality of knowledge bases to form a knowledge base process, wherein the key technology of integration comprises reference resolution, entity disambiguation and entity linking;
different knowledge bases have different emphasis points for collecting knowledge, and the description of the entity by the different knowledge bases is integrated to obtain the complete description of the entity.
The invention also provides a system for establishing the examination knowledge graph relationship, which is characterized by comprising the following steps:
the map data source acquisition module is used for acquiring a knowledge map data source;
the relation extraction module is used for extracting the incidence relation between the entities from the related linguistic data of the knowledge graph data source;
the knowledge integration module is used for integrating the knowledge in the knowledge bases according to the incidence relation to form an integral knowledge base;
and the knowledge processing module is used for carrying out knowledge processing on the whole knowledge base.
The invention provides a method and a system for establishing examination knowledge graph relation, wherein the method comprises the following steps: acquiring a knowledge graph data source; extracting the association relation between the entities from the related corpora of the knowledge graph data source; integrating knowledge in a plurality of knowledge bases according to the incidence relation to form an integral knowledge base; and carrying out knowledge processing on the whole knowledge base. The method solves the problem that data in the field is dispersed in a plurality of systems, the data is various and complex, isolated and the single data is not high in value, and builds visual and structured knowledge, so that learners can clearly determine the targets, progress, paths and feedback of the learners, and the learning efficiency is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a test knowledge graph relationship establishing method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an examination knowledge graph relationship establishing system according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terms "comprises" and "comprising," and any variations thereof, in the present description and claims and drawings are intended to cover a non-exclusive inclusion, such as a list of steps or elements.
The technical solution of the present invention is further described in detail with reference to the accompanying drawings and embodiments.
As shown in fig. 1, the practitioner enters knowledge points; a professional inputs the incidence relation between the knowledge points; a visualization engine generates a knowledge graph; a user views the knowledge graph; a user sets a learning target of a knowledge point; each knowledge point presents different states according to the learning effect of the user; the user checks the learning states of all target knowledge points and masters the learning progress; and performing remedial learning aiming at weak knowledge points.
As shown in fig. 2, the knowledge graph system is developed based on BS architecture by adopting a mode of front-end and back-end separation, and the application technologies include main stream technologies of Java, Python, SpringBoot, MyBatis, SpringCloud, JPA, Redis, MongoDB, MySQL, OSS, Docker, kubernets, VUE, ElementUI, Nacos, and Sentinel. The method is deployed in a Kubernetes cluster, dynamic elastic expansion and contraction capacity is carried out according to the access flow of a user, and infinite expansion capability is achieved on the basis of controllable cost.
Knowledge graph systems generate knowledge graph data in three ways: the first is a manual mode, and teachers enter the well-organized knowledge points and the relations among the knowledge points into a knowledge map system in a manual entering mode to form a basic knowledge map. And secondly, extracting knowledge points and relations among the knowledge points from teaching materials, teaching and assisting materials and audio and video materials of a large number of registered accountants for examination by adopting an artificial intelligence correlation technique to generate an auxiliary knowledge map. And the third method is that the supplementary knowledge graph is generated by capturing the information of the third party and capturing the relevant data of the examination of the registered accountant in the Internet and carrying out ETL processing. And finally, fusing the three knowledge maps to form a high-quality complete registered accountant examination knowledge map.
And forming a trainee personalized knowledge point sub-graph based on the complete knowledge graph and the user portrait data of the trainee. The common collaborative filtering algorithm in the recommendation system is used and rich semantic information in the knowledge map is combined, the most suitable knowledge points and learning paths are recommended to the trainees, the pertinence and the accuracy of learning knowledge of the trainees are higher, and the purpose of rapidly mastering the knowledge is achieved.
Data acquisition is the first step in establishing a knowledge-graph. At present, the knowledge-graph data sources are divided into two types according to different source channels: one is the data of the business itself, and the part of the data is usually contained in an internal database table and stored in a structured manner, and is non-public or semi-public data; the other is data published and captured on the network, which usually exists in the form of web pages and is unstructured data.
According to different data structures, the method is divided into three types: and processing the structured data, the semi-structured data and the unstructured data by adopting different methods according to different data types.
The information extraction specifically comprises the following steps: the text corpus is subjected to entity extraction to obtain a series of discrete named entity nodes, and in order to obtain semantic information, the association relationship between entities is extracted from related corpus so as to link a plurality of entities or concepts to form a mesh knowledge structure. The research on the relation extraction technology is to research how to extract the relation between entities from the text corpus.
The knowledge fusion comprises the following steps: the relationship between information units after information extraction is flat, and the information units lack hierarchy and logic, and a large amount of redundant and even wrong information fragments exist. Knowledge fusion, which is a process of integrating knowledge in a plurality of knowledge bases to form one knowledge base, is simply understood, and in the process, the main key technologies include reference resolution, entity disambiguation and entity linking. Different knowledge bases have different emphasis points on knowledge collection, for the same entity, the description of a certain aspect of the same entity may be emphasized by the knowledge base, the relationship between the entity and other entities may be emphasized by the knowledge base, and the purpose of knowledge fusion is to integrate the description of the entity by the different knowledge bases, so as to obtain the complete description of the entity.
The knowledge processing comprises the following steps: the mass data is subjected to information extraction and knowledge fusion to obtain a series of basic fact expressions, but the basic fact expressions are not equal to knowledge, and qualified parts can be incorporated into a knowledge system to ensure the quality of a knowledge base after a structured and networked knowledge system is obtained and quality evaluation is carried out, so that the knowledge processing process is realized.
Has the advantages that: developing a knowledge graph of registered accountants, structuring heterogeneous knowledge in the field, and constructing the association between knowledge. The method mainly solves the problems that data in the field are dispersed in a plurality of systems, the data are various and complex, isolated and the single data are not high in value, and builds visual and structured knowledge, so that learners can clearly determine targets, progress, paths and feedback of the learners, and the learning efficiency is improved.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. A test knowledge graph relation establishing method is characterized by comprising the following steps:
acquiring a knowledge graph data source;
extracting the association relation between the entities from the related corpora of the knowledge graph data source;
integrating knowledge in a plurality of knowledge bases according to the incidence relation to form an integral knowledge base;
and carrying out knowledge processing on the whole knowledge base.
2. The examination knowledge graph relationship establishing method according to claim 1, wherein the knowledge processing of the entire knowledge base specifically comprises:
and after quality evaluation, the qualified part is incorporated into a knowledge system to ensure the quality of the knowledge base.
3. The examination knowledge-graph relationship establishing method according to claim 1, wherein the acquiring a knowledge-graph data source specifically comprises:
the knowledge graph data source is divided into two types according to source channels, and the two types comprise: one is the data of the business itself, which is contained in an internal database table and stored in a structured way, and is non-public or semi-public data; the other is data which is published and captured on the network, and the data usually exists in the form of web pages and is unstructured data;
the knowledge graph data source is divided into structured data, semi-structured data and unstructured data according to different data structures;
and processing by adopting a corresponding method according to different data types.
4. The examination knowledge-graph relationship establishing method according to claim 1, wherein the extracting of the association relationship between the entities from the related corpora of the knowledge-graph data source specifically comprises:
acquiring text corpora from the related corpora of the knowledge graph data source;
the text corpus is subjected to entity extraction to obtain a series of discrete named entity nodes;
and extracting the association relation among the entities from the related linguistic data, and associating a plurality of entities or concepts to form a reticular knowledge structure.
5. The examination knowledge graph relationship establishing method according to claim 1, wherein the integrating knowledge in a plurality of knowledge bases according to the association relationship to form an overall knowledge base specifically comprises:
integrating knowledge in a plurality of knowledge bases to form a knowledge base process, wherein the key technology of integration comprises reference resolution, entity disambiguation and entity linking;
different knowledge bases have different emphasis points for collecting knowledge, and the description of the entity by the different knowledge bases is integrated to obtain the complete description of the entity.
6. An examination knowledge graph relationship establishing system, characterized in that the establishing system comprises:
the map data source acquisition module is used for acquiring a knowledge map data source;
the relation extraction module is used for extracting the incidence relation between the entities from the related linguistic data of the knowledge graph data source;
the knowledge integration module is used for integrating the knowledge in the knowledge bases according to the incidence relation to form an integral knowledge base;
and the knowledge processing module is used for carrying out knowledge processing on the whole knowledge base.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116028647A (en) * | 2023-02-07 | 2023-04-28 | 中科乐听智能技术(济南)有限公司 | Knowledge-graph-based fusion education intelligent comment method and system |
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| CN111444351A (en) * | 2020-03-24 | 2020-07-24 | 清华苏州环境创新研究院 | A method and device for constructing knowledge graph in industrial process field |
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| CN111831908A (en) * | 2020-06-24 | 2020-10-27 | 平安科技（深圳）有限公司 | Method, device, equipment and storage medium for constructing knowledge graph in medical field |
| CN113886567A (en) * | 2021-08-31 | 2022-01-04 | 安徽商贸职业技术学院 | Teaching method and system based on knowledge graph |
| CN113918732A (en) * | 2021-11-19 | 2022-01-11 | 北京明略软件系统有限公司 | Multi-modal knowledge graph construction method and system, storage medium and electronic equipment |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111444351A (en) * | 2020-03-24 | 2020-07-24 | 清华苏州环境创新研究院 | A method and device for constructing knowledge graph in industrial process field |
| CN111831908A (en) * | 2020-06-24 | 2020-10-27 | 平安科技（深圳）有限公司 | Method, device, equipment and storage medium for constructing knowledge graph in medical field |
| CN111767440A (en) * | 2020-09-03 | 2020-10-13 | 平安国际智慧城市科技股份有限公司 | Vehicle portrayal method based on knowledge graph, computer equipment and storage medium |
| CN113886567A (en) * | 2021-08-31 | 2022-01-04 | 安徽商贸职业技术学院 | Teaching method and system based on knowledge graph |
| CN113918732A (en) * | 2021-11-19 | 2022-01-11 | 北京明略软件系统有限公司 | Multi-modal knowledge graph construction method and system, storage medium and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116028647A (en) * | 2023-02-07 | 2023-04-28 | 中科乐听智能技术(济南)有限公司 | Knowledge-graph-based fusion education intelligent comment method and system |
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