An information support framework for chain reaction of major emergencies based on causality eventic graph

Purpose With the rapid development of society, major emergencies occur frequently, posing a serious threat to people’s lives and property. This study focuses on the chain reaction of major emergencies, aiming to improve the overall situational awareness and capabilities for managing major emergencies in complex scenarios. Design/methodology/approach We proposed an information support framework for the chain reaction of major emergencies based on a causality eventic graph (CEG). The framework consists of three modules: the data layer, the analysis layer and the service layer. The data layer focuses on the perception and collection of major emergency information. The analysis layer includes key components such as causality recognition, causality extraction, event fusion and generalization. In this layer, we developed several deep learning (DL)-based models using a joint extraction approach to obtain causal pairs. The service layer depicts the event evolution logic from both industry and public perspectives. Findings The empirical study has demonstrated the feasibility and effectiveness of the proposed information support framework. First, the BERT-BiLSTM-CRF model achieved the best performance in the causality extraction task. Second, the SBERT model was found to be more suitable for event fusion. Third, the analysis results of CEG revealed that the impact of pandemics on industries in turn affects other industries as well as people’s livelihoods and vice versa. The framework shows a better information support, discovery and reasoning effect. This study, however, still has several limitations. We focused primarily on causal logic and did not fully explore other logical relationships. In future work, we will incorporate more logical relations to further refine the eventic graph (EG). Originality/value This study proposes a novel information support framework for the chain reaction of major emergencies, leveraging CEG to provide targeted and hierarchical information to industry and public stakeholders. It solves several problems in the emergency management of major emergencies.