Abstract:

In order to assist earthquake rescue personnel in enhancing disaster response speed and adapting to diverse search and rescue needs, an intelligent management method for earthquake rescue equipment information based on a knowledge graph was proposed. Through the top-down knowledge graph construction method, earthquake rescue knowledge was first obtained from various information sources to serve as the basis for knowledge modeling. Next, a rule-based method was used to extract search and rescue knowledge, which was then integrated based on cosine similarity. The integrated knowledge was stored in the form of Resource Description Framework (RDF) triples. Subsequently, the open-source graph database Neo4j was employed to organize the triples into a visualized knowledge graph. Finally, a question-and-answer system was built based on the knowledge graph, allowing users to query the knowledge on the graph using natural language. The results indicate that the knowledge graph includes five categories of entities and relationships: disasters, secondary disasters, environmental factors, rescue needs, and rescue equipment. It facilitates quick matching of equipment based on rescue needs. The knowledge graph-based method can effectively manage and schedule rescue equipment information, improving the efficiency of the preparation phase of rescue operations.

Key words: knowledge graph, earthquake rescue, rescue equipment, intelligent management, knowledge extraction, knowledge fusion, Neo4j