Construction of knowledge graph ontology in the field of fine-grained early warning equipment

Abstract

To solve the problems of unstructured data organization, insufficient detailed description of entity relationship and lack of standardized expression of knowledge representation in the construction of knowledge graph in the field of early warning equipment. Based on the Protégé ontology construction tool, this paper divides the early warning equipment system from top to bottom into equipment principle knowledge class, management support resource class, equipment operation and application class, etc., and then refines them. So as to define the concept class and divide the hierarchical relationship, and extract the entities, attributes, relationships and other knowledge units in specific equipment information. Then, the domain knowledge of early warning equipment is modeled, and a scientific and complete knowledge representation framework of fine-grained early warning equipment is explored. Taking “long-range early warning phased array radar” as an example, ontology instance filling and visualization are carried out. Finally, the ontology of early warning equipment is formally represented based on ontology description language. The ontology lays a semantic foundation for the construction of domain knowledge graph based on multi-source high-quality data, and has important practical guiding value for realizing intelligent management support and operational application of equipment such as equipment capability portrait, development trend analysis, equipment fault diagnosis, and health status management under the background of big data.

Keywords

• early warning equipment|ontology construction|knowledge graph|fine-grained