Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (6): 1215-1227.doi: 10.12082/dqxxkx.2023.210696

Previous Articles     Next Articles

Research and Practice on the Framework for the Construction, Sharing, and Application of Large-scale Geoscience Knowledge Graphs

ZHU Yunqiang1,2,7(), SUN Kai1,*(), HU Xiumian3, LV Hairong4,5, WANG Xinbing6, YANG Jie1, WANG Shu1, LI Weirong1,7, SONG Jia1,2, SU Na1, MU Xinglin8   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    3. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
    4. Department of Automation, Tsinghua University, Beijing 100084, China
    5. Fuzhou Institute for Data Technology, Fuzhou 350207, China
    6. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    7. University of Chinese Academy of Sciences, Beijing 100049, China
    8. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
  • Received:2021-11-01 Revised:2022-01-29 Online:2023-06-25 Published:2023-06-02
  • Contact: *SUN Kai, E-mail:
  • Supported by:
    National Natural Science Foundation of China(42050101);National Natural Science Foundation of China(41771430);National Natural Science Foundation of China(41631177);Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23100100)


Geoscience Knowledge Graph (GKG) has strong capabilities of knowledge representation and semantic reasoning, thereby becoming a required infrastructure for the development of geoscience big data and geoscience artificial intelligence. However, existing studies on GKG were mainly conducted under the experimental scenarios. Because of a lack of research on the general framework of construction methods, sharing, and application of large-scale GKG for practical applications, it has not been used in practical applications in the geoscience field. For this reason, towards the needs of research and applications of geoscience big data and artificial intelligence for GKG, this paper first studied the construction techniques of large-scale GKG. Then, a general framework for covering the lifecycle of GKG including its construction, sharing, and application was proposed. Taking the big science program “Deep-Time Digital Earth (DDE)” as an example, the practice of developing GKG platform towards the practical application of DDE was carried out. Using this platform, this paper realized the construction of DDE large-scale GKG, the open sharing and application of built GKG, proving that the proposed framework can effectively support the construction, sharing, and application of large-scale GKG. This paper plays an important role in promoting the realization of the practical application value of GKG.

Key words: geoscience knowledge graph, geoscience ontology, knowledge mining, knowledge representation, knowledge sharing, knowledge service, knowledge application