Abstract:

The causality causal graph of hazardous chemical accidents was developed to improve the safety management level of hazardous chemical enterprises. Firstly, based on the accident investigation report, an entity-relationship joint extraction model was proposed through an improved CasRel technique. Furthermore, the proposed model aimed to improve the extraction accuracy of textual information by incorporating the relationship-aware bidirectional encoder representation method (R-Bert) and Span pointer network. Subsequently, similarity calculation methods were used to generalize the events to enhance the graph's comprehensiveness and accuracy. Then, the refined data was stored in the Neo4j graph database visualizing the associations between events. Finally, the corresponding guestion-answering system was proposed based on the developed causal graph, and then an intelligent question-answering system for the causality of hazardous chemical accidents was proposed. The results indicated that the F₁ value calculated by the improved CasRel model was 90.5%, and the prediction accuracy of the proposed model was 2% higher than that simulated by the original model. The hazardous chemical accidents causal graph and intelligent question-answering system performed well in terms of multiple evaluation indexes, clearly revealing the logical relationship between events. Therefore, the proposed model in this study can meet question-answering needs of hazardous chemical accidents, facilitating the exploration of accident patterns and potential risk factors, and enabling accident trend prediction.

Key words: hazardous chemical accidents, casual graph, knowledge extraction, causality, intelligent question-answering system