Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports  

作　　者：Qinjun Qiu Zhen Huang Dexin Xu Kai Ma Liufeng Tao Run Wang Jianguo Chen Zhong Xie Yongsheng Pan 

机构地区：[1]Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,Shenzhen 518034,China [2]Key Laboratory of Geological Survey and Evaluation of Ministry of Education,China University of Geosciences,Wuhan 430074,China [3]Beijing Key Laboratory of Urban Spatial Information Engineering,Beijing 100045,China [4]School of Geography and Information Engineering,China University of Geosciences,Wuhan 430074,China [5]Wuhan Geomatics Institute,Wuhan 430074,China [6]College of Computer and Information Technology,China Three Gorges University,Yichang 443002,China [7]Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering,China Three Gorges University,Yichang 443002,China [8]Geological Environmental Center of Hubei Province,Wuhan 430034,China [9]Faculty of Earth Resources,China University of Geosciences,Wuhan 430074,China [10]State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan 430078,China [11]Wuhan Zondy Cyber Science&Technology Co.,Ltd.,Wuhan 430074,China

出　　处：《Journal of Earth Science》2023年第5期1433-1446,共14页地球科学学刊（英文版）

基　　金：the IUGS Deep-time Digital Earth (DDE) Big Science Program;financially supported by the National Key R & D Program of China (No.2022YFB3904200);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。

摘　　要：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.

关 键 词：geological hazard report spatiotemporal information geological hazard ontology natural language processing GAZETTEERS onlology machine learning 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] P694[自动化与计算机技术—计算机科学与技术]