Construction and application of integrated knowledge graph for mine disasters

HE Yabo

doi:10.13347/j.cnki.mkaq.20250025

Abstract：In order to achieve a novel disaster early warning mode of"autonomous modeling+integrated early warning+root cause tracing",and improve the knowledge engineering infrastructure for integrated intelligent disaster warning,this study developed an ontology model for integrated knowledge graphs of disaster based on the"human-machine-environment"system engineering the-ory.The model was constructed through a top-down approach across four dimensions:temporal,spatial,managerial,and process mechanisms.It encompasses eight core concepts in this field of personnel,equipment,environment,region,process,document,in-dex,and disaster,along with three categories of data attributes(basic information,spatial information,and temporal information).Three types of relationships were established:spatial positioning,numerical correlations,and process mechanism linkages.A hybrid data storage architecture integrating relational,spatial,temporal,and graph databases was built.Data extraction for entities,attrib-utes,and relationships was achieved through a combination of rule-driven workflow engines and manual data supplementation,form-ing mine-specific disaster-integrated knowledge graphs.By adopting graph database relationship reasoning methods,coupled with anomaly identification criteria for graph objects,root cause analysis of mine disasters was realized.The results show that a"top-down"knowledge graph construction scheme that involves expert modeling and regularized data extraction is suitable in the early stage in the field of integrated analysis of mine disasters;the framework of"eight core concepts,three data attributes,and three rela-tionship types"significantly enriches disaster knowledge systems;the hybrid methodology of rule-driven engines and manual sup-plementation effectively addresses the needs of mines at varying intelligentization stages;knowledge graph relationship reasoning in-tegrated with object attribute anomaly detection provides a robust technical solution for accident causation analysis.