Constructing Dynamic Knowledge Graph Based on Ontology Modeling and Neo4j Graph Database
Yaxi Chen, Xuefeng Xing · 2022
At present, there are several issues with large-scale domain dynamic knowledge graphs including incomplete acquisition of original data, low accuracy with knowledge extraction and knowledge fusion, as well as un nonuniform semantic relations between entities. This paper constructs dynamic knowledge graph based on ontology modeling and Neo4j graph database. The ontology data model built based on the “seven-step method” effectively avoids the filling of instances without concept classes in the original data, while removing concepts with low user attention or learning value, which ensures integrity of original data acquisition, efficiency and accuracy of knowledge extraction and fusion, as well as rationality of logical relations between classes. Based on the ontology constraints and the mapping between the ontology model and Neo4j graph database, large-scale domain dynamic knowledge graph is achieved. We apply this scheme in the field of agricultural informatization and receive satisfying experimental results. In future work, we plan to explore multi-modal dynamic knowledge graph.