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

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

doi:10.1007/s12583-022-1716-z

Volume 34 Issue 5

Oct 2023

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Journal of Earth Science > 2023 > 34(5): 1433-1446.

Qinjun Qiu, Zhen Huang, Dexin Xu, Kai Ma, Liufeng Tao, Run Wang, Jianguo Chen, Zhong Xie, Yongsheng Pan. Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports. Journal of Earth Science, 2023, 34(5): 1433-1446. doi: 10.1007/s12583-022-1716-z

Citation:

Qinjun Qiu, Zhen Huang, Dexin Xu, Kai Ma, Liufeng Tao, Run Wang, Jianguo Chen, Zhong Xie, Yongsheng Pan. Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports. Journal of Earth Science, 2023, 34(5): 1433-1446. doi: 10.1007/s12583-022-1716-z

Citation:

Qinjun Qiu, Zhen Huang, Dexin Xu, Kai Ma, Liufeng Tao, Run Wang, Jianguo Chen, Zhong Xie, Yongsheng Pan. Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports. Journal of Earth Science, 2023, 34(5): 1433-1446. doi: 10.1007/s12583-022-1716-z

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

doi: 10.1007/s12583-022-1716-z

Qinjun Qiu^{1, 2, 3
,},
Zhen Huang⁴,
Dexin Xu⁵,
Kai Ma^{6, 7},
Liufeng Tao^{1, 2},
Run Wang^{8
,
,
,},
Jianguo Chen^{9, 10},
Zhong Xie^{1, 2},
Yongsheng Pan¹¹

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

More Information

Corresponding author: Run Wang, runwang@cug.edu.cn
Received Date: 06 Dec 2021
Accepted Date: 23 Jul 2022

Available Online: 14 Oct 2023

Issue Publish Date: 30 Oct 2023

Abstract

Abstract

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 pre-sents a combined NLP and ontology matching information extraction framework for automatically re-cognizing 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.
- geological hazard report,
- spatiotemporal information,
- geological hazard ontology natural language processing,
- gazetteers,
- onlology,
- machine learning

Conflict of Interest
The authors declare that they have no conflict of interest.

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