CN-116523041-B - Knowledge graph construction method, retrieval method and system for equipment field and electronic equipment

CN116523041BCN 116523041 BCN116523041 BCN 116523041BCN-116523041-B

Abstract

The invention discloses a knowledge graph construction method, a retrieval method, a system and electronic equipment in the equipment field, and relates to the technical field of knowledge graphs, wherein the knowledge graph construction method in the equipment field comprises the steps of constructing an equipment field body based on an equipment field original data set; based on the original data set of the equipment field and the body of the equipment field, extracting and combining the entity and the relation into a whole information extraction task to process, outputting triplet data in an end-to-end mode, and constructing an equipment field knowledge graph according to the triplet data. The invention can build a corresponding equipment knowledge system, and provide more intelligent and personalized services by combining knowledge extraction, information retrieval and other technologies so as to promote the digital and visual development of the equipment field.

Inventors

CHENG BO
GUO XIAO
LI XIANG
GU WENYUAN

Assignees

北京邮电大学

Dates

Publication Date: 20260508
Application Date: 20230506

Claims (7)

1. The knowledge graph construction method in the equipment field is characterized by comprising the following steps of: Constructing an equipment field original data set; The equipment field ontology is constructed based on an equipment field original data set, and the equipment field ontology is constructed based on the equipment field original data set, specifically comprising a top-down ontology modeling scheme and a bottom-up ontology modeling scheme, wherein the top-down ontology modeling scheme comprises the steps of summarizing the equipment field ontology of which the pattern layer is designed by means of the prior knowledge of the equipment field industry, and carrying out the top-down extraction operation on the data in the equipment field original data set according to the designed pattern layer to realize the construction of the equipment field ontology; Based on the original data set of the equipment field and the body of the equipment field, extracting and combining the entity and the relation into a whole information extraction task for processing, and outputting the triplet data in an end-to-end mode; the method comprises the steps of integrating entity and relation extraction into a whole information extraction task for processing based on an equipment field original data set and an equipment field body, and outputting triplet data in an end-to-end mode, and specifically comprises the following steps: based on the original data set of the equipment field and the body of the equipment field, adopting a knowledge joint extraction algorithm based on a Seq-to-Seq frame and a RoBERTa model to extract and combine the entity and the relation into a whole information extraction task for processing, and outputting the triplet data in an end-to-end mode; the method comprises the steps of mapping entities and relations in the triple data into nodes and edges in the maps by using a Cypher grammar, and importing the constructed knowledge map of the equipment field into a Neo4j map database for storage.
2. The method for constructing an equipment domain knowledge graph according to claim 1, wherein the constructing an equipment domain raw data set specifically comprises: and capturing webpage data of the professional weapon equipment by adopting a crawler technology, and storing the captured data as a JSON format file to establish an original data set in the field of construction equipment.
3. The retrieval method based on the knowledge graph in the equipment field is characterized by comprising the following steps of: According to the search keywords, information retrieval is carried out on the equipment domain knowledge graph determined by the method according to any one of claims 1-2 based on an information retrieval strategy of node matching and query expansion, wherein the node matching refers to judging whether the search keywords can be mapped to the entities in the equipment domain knowledge graph, and the query expansion refers to carrying out organic expansion on the entities in the equipment domain knowledge graph so as to find out the entities similar to the search keywords.
4. The knowledge graph construction system in the equipment field is characterized by comprising: the data set construction module is used for constructing an original data set in the equipment field; The equipment field ontology construction module is used for constructing an equipment field ontology based on an equipment field original data set, and specifically comprises a top-down ontology modeling scheme and a bottom-up ontology modeling scheme, wherein the top-down ontology modeling scheme is used for summarizing the equipment field ontology of a mode layer by means of equipment field industry priori knowledge and extracting data in the equipment field original data set from top to bottom according to the equipment field ontology of the designed mode layer to realize construction of the equipment field ontology; The system comprises an equipment field original data set, an equipment field body, a three-tuple data extraction module, a data processing module and a data processing module, wherein the equipment field original data set and the equipment field body are based on an information extraction task which combines entity and relation extraction into a whole and outputs three-tuple data in an end-to-end mode; The equipment field knowledge graph construction module is used for constructing an equipment field knowledge graph according to the triplet data, and the equipment field knowledge graph construction module specifically comprises the steps of mapping entities and relations in the triplet data into nodes and edges in the graph by using a Cypher grammar, and importing the constructed equipment field knowledge graph into a Neo4j graph database for storage.
5. The retrieval system based on the knowledge graph in the equipment field is characterized by comprising: The information retrieval module is used for retrieving information from the equipment domain knowledge graph determined by the method according to any one of claims 1-2 based on node matching and query expansion information retrieval strategies according to the search keywords, wherein the node matching refers to judging whether the search keywords can be mapped to the entities in the equipment domain knowledge graph, and the query expansion refers to organically expanding the entities in the equipment domain knowledge graph to find out the entities similar to the search keywords.
6. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform an equipment domain knowledge graph construction method according to any one of claims 1 to 2.
7. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform a method of retrieving an equipment domain knowledge-graph based on claim 3.

Description

Knowledge graph construction method, retrieval method and system for equipment field and electronic equipment Technical Field The invention relates to the technical field of knowledge graphs, in particular to a knowledge graph construction method, a knowledge graph retrieval system and electronic equipment in the equipment field. Background Knowledge graph is a technique that organizes entities, concepts and relationships into a structured knowledge network. The multi-source heterogeneous knowledge is integrated and represented, so that a machine can understand and infer human knowledge, and further application in the artificial intelligence fields such as semantic understanding, natural language processing, question answering and the like is realized. The knowledge graph construction needs to solve the problems of multi-source knowledge extraction, knowledge storage and the like, and meanwhile needs to consider the problems of knowledge consistency, completeness, reliability and the like. The domain knowledge graph is the application of the knowledge graph in a specific domain, and aims to construct a graph with rich knowledge and semantic relations in the specific domain, and is used for supporting various intelligent applications in the domain. The construction of the domain knowledge graph relates to knowledge extraction, knowledge storage and other technologies. Knowledge extraction refers to extracting entities and relations related to the field from unstructured data, and knowledge storage refers to persistence of knowledge extracted from different data sources in a unified knowledge graph form. The domain knowledge graph retrieval needs to combine the technologies of semantic matching, path searching and the like to quickly and accurately retrieve related entities and knowledge from the graph, thereby providing accurate answers and services for users. In the domain knowledge graph, because of various complex relationships among entities, the traditional retrieval mode based on keyword matching cannot meet the requirements. Therefore, research and application of domain knowledge graph retrieval have important significance for promoting intelligent development of various domains. A large amount of unstructured data is accumulated in the field of equipment today, hiding much of the information available. However, the existing equipment data is organized in disorder and stored in various databases and websites, and when facing to the large-scale equipment information, the related field personnel often need to spend a great deal of time and effort on data research and reading, so that it is difficult to acquire key information in real time. Disclosure of Invention The invention aims to provide a knowledge graph construction method, a retrieval method, a system and electronic equipment in the equipment field, which are used for constructing a corresponding equipment knowledge system and providing more intelligent and personalized services by combining knowledge extraction, information retrieval and other technologies so as to promote the digital and visual development of the equipment field. In order to achieve the above object, the present invention provides the following solutions: in a first aspect, the present invention provides a method for constructing a knowledge graph in an equipment field, including: Constructing an equipment field original data set; Constructing an equipment field body based on the equipment field original data set; Based on the original data set of the equipment field and the body of the equipment field, extracting and combining the entity and the relation into a whole information extraction task for processing, and outputting the triplet data in an end-to-end mode; and constructing a knowledge graph of the equipment field according to the triplet data. In a second aspect, the invention provides a retrieval method based on knowledge graph in equipment field, comprising the following steps: According to the search keywords, information retrieval is carried out on the equipment domain knowledge graph determined in the first aspect based on an information retrieval strategy of node matching and query expansion, wherein the node matching refers to judging whether the search keywords can be mapped to the entities in the equipment domain knowledge graph, the query expansion refers to carrying out organic expansion on the entities in the equipment domain knowledge graph, and the entities similar to the search keywords are found out. In a third aspect, the present invention provides a knowledge graph construction system in an equipment field, including: the data set construction module is used for constructing an original data set in the equipment field; The equipment field body construction module is used for constructing an equipment field body based on the equipment field original data set; the triple data extraction module is used for processing the entity and relation extraction a