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
基金the IUGS Deep-time Digital Earth (DDE) Big Science Programfinancially supported by the National Key R & D Program of China (No.2022YFB3904200)+4 种基金the Natural Science Foundation of Hubei Province of China (No.2022CFB640)the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources (No.KF-202207-014)the Opening Fund of Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering (No.2022SDSJ04)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (No.GLAB 2023ZR01)the Fundamental Research Funds for the Central Universities。
文摘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.
