Development of knowledge graphs for emergency events utilizing pre-trained language models

Emergency management requires structured representations that can integrate heterogeneous event reports and support cross-event analysis. This study develops an emergency event knowledge graph (EE-KG) by combining fine-grained event classification, lifecycle-oriented ontology modeling, and ontology-guided knowledge extraction. We collected 6,418 Chinese emergency-related reports from public government portals, news websites, and safety information platforms published between 2000 and 2024, and aggregated them into 1,971 event records. A manually annotated multi-label dataset was constructed according to four primary categories and 36 subcategories. Four classification strategies were evaluated: fine-tuned ERNIE 3.0, prompt-based GLM-4-9B-Chat, agent-based GLM-4-9B-Chat with reflective reasoning, and fine-tuned GLM-4-9B-Chat. The fine-tuned GLM-4-9B-Chat achieved the best performance, with an accuracy of 0.80 and a micro-F1 score of 0.85. Classification outputs were used to assign standardized event types to event nodes, while a five-dimensional ontology guided the extraction of event description, impact assessment, emergency response, recovery and reconstruction, and evaluation information. The resulting EE-KG contains 16,057 nodes and 18,428 relationships. Expert review indicates that the proposed framework provides a reusable structure for emergency event knowledge organization and analysis.