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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Science China Earth Sciences volume 66, pages 2563–2578 (2023)Cite this article

Abstract

Geoscience knowledge graph (GKG) can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services. As a result, it has gained significant attention and become a frontier in geoscience. Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales, granularities, and dimensions. Therefore, establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG. However, existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships. To address this issue, this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge. On this basis, an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships. Under the constraint of a unified spatiotemporal ontology, this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation. This model can efficiently represent geoscience knowledge, thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval. It can further enable the alignment, transformation, computation, and reasoning of spatiotemporal information through a spatiotemporal ontology.

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Acknowledgements

We would like to extend our thanks to the principal investigators of DDE, Academician Chengshan Wang and Academician Qiuming Cheng, for their guidance and valuable comments. This work was supported by the National Natural Science Foundation of China (Grant No. 42050101) and the National Key Research and Development Program of China (Grant Nos. 2022YFB3904200 & 2021YFB00903). This work was also supported by the International Big Science Program of Deep-time Digital Earth (DDE).

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Authors and Affiliations

  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

    Yunqiang Zhu, Kai Sun, Shu Wang, Chenghu Zhou, Feng Lu & Yanmin Qi

  2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China

    Yunqiang Zhu

  3. Department of Automation, Tsinghua University, Beijing, 100084, China

    Hairong Lv

  4. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Qinjun Qiu

  5. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xinbing Wang

  6. School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China

    Yanmin Qi

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  1. Yunqiang Zhu
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Correspondence to Yunqiang Zhu or Kai Sun.

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Zhu, Y., Sun, K., Wang, S. et al. An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships. Sci. China Earth Sci. 66, 2563–2578 (2023). https://doi.org/10.1007/s11430-022-1169-9

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