Two-Stage Construction Method of Event Knowledge Graph for Emergency Disposal of Gas Accidents

Aiming at the ambiguous and unclarity logical relationship among disposal text information in gas emergencies, a two-stage method focusing on gas accident case text is proposed for constructing the event knowledge graph of gas emergency disposal. First, a gas text joint extraction method (GTJM) for gas accident cases is proposed at the first stage. The multigranularity training tasks in the enhanced representation through knowledge integration (ERNIE) pretrained model are used to address the issue of insufficient context understanding when processing long texts in gas cases while combining multihead attention mechanism to obtain richer semantic information. The cascade binary tagging framework (CasRel) is then utilized to extract entities and relationships contained in the background and disposal information of accident cases, to tackle the problem of overlapping entity extraction in gas accident case texts. Second, in the second stage, the bidirectional encoder representation from transformers (BERT) is used to capture the contextual features of compressed events, while a novel Incept-text convolutional neural network (TextCNN) focuses on the global semantic information within compressed events. The combination of both models could resolve the problem of sequential relationship identification between long events much better. Finally, a clear event logic knowledge graph is formed for gas accident disposal in emergencies. The experimental results indicate that in the first phase, the GTJM model has an average improvement of 7% in F1 value compared to other baseline models. In the second phase, the accuracy of the sequential relationship identification reaches 92%. The feasibility of the proposed method is verified through real cases. The study could provide auxiliary decision support for emergency disposal in gas accidents.