Construction and application of knowledge graph for urban agglomeration emergency rescue collaboration in earthquake disastersOA
This study proposes an emergency rescue collaboration knowledge graph construction method for urban agglomeration in earthquake disasters.Based on the collection of 22 earthquake disaster emergency plans published on the official websites of multiple cities in the Chengdu-Chongqing urban agglomeration in China,earthquake disaster emergency rescue data from the Red Cross Society of Sichuan Province and Chongqing City,and historical rescue information from the China Blue Sky rescue team,this study defines and extracts six types of entities including rescue entities,policy documents,rescue actions,rescue information,rescue supplies,and emergency response levels.A knowledge graph pattern layer is established using a hybrid approach of top-down and bottom-up,including concept layer,relationship layer,rule layer,and instance layer.This study extracts earthquake disaster emergency rescue collaboration knowledge information from collected data sources,and YEDDA software is used for knowledge fusion,thus constructing a knowledge graph data layer.The data is stored in the Neo4j graph database as triplets(entity-relation-entity).Visual representation and retrieval are used to achieve the query,association,and inference of emergency rescue collaboration information for urban agglomeration in earthquake disasters.The 2022 Luding earthquake disaster in Ganzi Tibetan Autonomous Prefecture,China is selected as a typical case,and verified the effectiveness and reliability by inputting the case into the emergency rescue collaboration knowledge graph which was constructed in this study.The results indicate that the constructed knowledge graph provides intelligent decision support for earthquake disaster emergency rescue collaboration in urban agglomeration,effectively improves the performance of earthquake disaster emergency rescue,and provides new ideas and methods for earthquake disaster rescue and reduction.
Lin Zhang;Qichen Wang;Yanjun Guo;Xiangliang Tian;Lin Qi
College of Management Sciences and Engineering,Beijing Information Science and Technology University,Beijing 100192,ChinaCollege of Management Sciences and Engineering,Beijing Information Science and Technology University,Beijing 100192,ChinaSchool of Intelligent Engineering and Automation,Beijing University of Posts and Telecommunications,Beijing 100876,ChinaKey Laboratory of Non-coal Mine Safety Risk Monitoring and Early Warning National Mine Safety Administration,China Academy of Safety Science andTechnology,Beiiing 100012,ChinaCollege of Management Sciences and Engineering,Beijing Information Science and Technology University,Beijing 100192,China
天文与地球科学
Knowledge graphEarthquake disasterUrban agglomerationEmergency rescue collaborationNeo4j graph database
《Journal of Safety Science and Resilience》 2026 (1)
P.250-263,14
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