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Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports
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作者 Qinjun Qiu Zhen Huang +6 位作者 Dexin Xu Kai Ma Liufeng Tao Run Wang Jianguo Chen Zhong Xie Yongsheng Pan 《Journal of Earth Science》 SCIE CAS CSCD 2023年第5期1433-1446,共14页
Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retri... Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events. 展开更多
关键词 geological hazard report spatiotemporal information geological hazard ontology natural language processing GAZETTEERS onlology machine learning
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