Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (6): 1091-1105.doi: 10.12082/dqxxkx.2023.230154

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Spatiotemporal Knowledge Graph: Advances and Perspectives

LU Feng1,2,4,5,*(), ZHU Yunqiang1,2,4, ZHANG Xueying3,4   

  1. 1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing 210023, China
    4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    5. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
  • Received:2023-03-27 Revised:2023-04-19 Online:2023-06-25 Published:2023-06-02
  • Contact: *LU Feng, E-mail:
  • Supported by:
    National Key Research and Development Program of China(2022YFB3904200);National Key Research and Development Program of China(2021YFB3900900);National Natural Science Foundation of China(41631177);National Natural Science Foundation of China(42050101)


The continuous generalization of geographic information poses a huge challenge to the classic geographic information analysis modes. Networked knowledge services will gradually become a new mode for geographic information applications, facilitating to transform the form of geographic computing into social computing. Geographic knowledge services need to connect people, institutions, natural environments, geographical entities, geographical units and social events, so as to promote knowledge assisted data intelligence and computational intelligence. Facing the urgent need for spatiotemporal knowledge acquisition, formal expression and analysis, this paper firstly introduces the concepts and characteristics of spatiotemporal knowledge graph. The spatiotemporal knowledge graph is a directed graph composed of geographic spatiotemporal distribution or geo-locational metaphors of knowledge that is a knowledge graph centered on spatiotemporal distribution characteristics. Secondly we proposes a research framework for spatiotemporal knowledge graph. The framework includes various levels from multimodal spatiotemporal big data to spatiotemporal knowledge services that contain ubiquitous spatiotemporal big data layer, spatiotemporal knowledge acquisition technique layer, spatiotemporal knowledge management layer, spatiotemporal knowledge graph layer, software/tools layer, and industrial application layer. Thirdly this paper introduces relevant research progress from text implied geographic information retrieval, heterogeneous geographic semantic web alignment, spatiotemporal knowledge formalization and representation learning. Combined with application practice, we then enumerate the construction and application approaches of domain oriented spatiotemporal knowledge graph. Finally, it discusses the key scientific issues and technical bottlenecks currently faced in the research of spatiotemporal knowledge graph. It is argued that in the era of large models, constructing explicit spatiotemporal knowledge graph and conducting knowledge reasoning to meet domain needs is still the only way for spatiotemporal knowledge services.

Key words: spatial-temporal knowledge, knowledge graph, GeoAI, knowledge modeling, spatial-temporal big data, information extraction, geo-semantic alignment, advances