ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-3/W2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3/W2-2022, 79–85, 2022
https://doi.org/10.5194/isprs-annals-X-3-W2-2022-79-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3/W2-2022, 79–85, 2022
https://doi.org/10.5194/isprs-annals-X-3-W2-2022-79-2022
 
27 Oct 2022
27 Oct 2022

RESEARCH ON THE CONSTRUCTION OF GEOGRAPHIC KNOWLEDGE GRAPH INTEGRATING NATURAL DISASTER INFORMATION

B. Zhang1, C. Yin1, K. Liu1, X. Zhai2, Y. Sun3, and M. Du1 B. Zhang et al.
  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
  • 2Information Service Department, National Geomatics Centre of China, Beijing 100830, China
  • 3State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

Keywords: Natural Disasters, Geographic Knowledge Graph, Knowledge Service, Knowledge Engineering, Natural Language Processing, Linked Data

Abstract. Natural disasters have a significant impact on the environment and economies of all countries around the world, and a large amount of multi-source heterogeneous geographic information data is generated every day. However, due to a lack of knowledge transformation capabilities, these nations continue to struggle with the issue of "a large amount of data and little knowledge". Therefore, it is of great significance how to extract geographic knowledge related to disasters from the vast data and construct a geographic knowledge graph integrating disaster information. Based on the theory related to knowledge extraction, this paper proposes a method to construct a natural disaster knowledge graph integrating geographic information. The core of this knowledge graph is to construct the association relationship between natural disaster concepts, research areas, and spatial data. The vocabulary and relationships associated with disaster concepts are primarily transformed by an existing word list of geographic narratives, which then provide rich semantic relationships of domain concepts for the entire knowledge graph. The research areas and spatial data types are mainly obtained through knowledge entity extraction and disambiguation methods. This disaster knowledge graph can support applications well such as natural disaster visualization and analysis, data recommendation systems, and intelligent Q&A systems, which can further improve the intelligence of natural disaster knowledge services and is expected to promote the sharing and reuse of domain knowledge graphs to a certain extent.