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China is one of the countries most affected by typhoon disasters. It is of great significance to study the mechanism of typhoon disasters and construct a typhoon disaster chain for emergency management and disaster reduction. The evolution process of typhoon disaster based on expert knowledge and historical disaster data has been summarized in previous studies, which relied too much on artificial experience while less in-depth consideration was given to the disaster exposure, the social environment, as well as the spatio-temporal factors. Hence, problems, such as incomplete content and inconsistent expression of typhoon disaster knowledge, have arisen. With the development of computer technology, massive Web corpus with numerous Web news and various improvised content on the social media platform, and ontology that enables consistent expression new light has been shed on the knowledge discovery of typhoon disaster. With the Chinese Web corpus as its source, this research proposes a method to construct a typhoon disaster chain so as to obtain disaster information more efficiently, explore the spatio-temporal trends of disasters and their impact on human society, and then comprehensively comprehend the process of typhoon disaster. First, a quintuple structure (Concept, Property, Relationship, Rule and Instance) is used to design the Typhoon Disaster Chain Ontology Model (TDCOM) which contains the elements involved in a typhoon disaster. Then, the information extraction process, regarded as a sequence labeling task in the present study, is combined with the BERT model so as to extract typhoon event-elements from the customized corpus. Finally, taking Typhoon Mangkhut as an example, the typical typhoon disaster chain is constructed by data fusion and structured expression. The results show that the methods presented in this research can provide scientific support for analyzing the evolution process of typhoon disasters and their impact on human society.

Research on the Construction of Typhoon Disaster Chain Based on Chinese Web Corpus