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Geoscience knowledge graph in the big data era

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Abstract

Since the beginning of the 21st century, the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means. It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph. Based on adopting the graph pattern of general knowledge representation, the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge, and integrates geoscience knowledge elements, such as map, text, and number, to establish an all-domain geoscience knowledge representation model. A federated, crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here, which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists. We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis, which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph. A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis, but also advance the construction of the high-precision geological time scale driven by big data, the compilation of intelligent maps driven by rules and data, and the geoscience knowledge evolution and reasoning analysis, among others. It will further expand the new directions of geoscience research driven by both data and knowledge, break new ground where geoscience, information science, and data science converge, realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.

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Acknowledgements

This paper benefitted from the guidances and supports from many domestic and overseas colleagues. The theme of this paper comes from our collective thinking. So this paper should be a piece of collective work. The authors want to specially thank the experts and scholars who participated in the Shuang Qing Forum themed “Data-driven Geoscience: from Tradition to the Data Age” in 2019 and Deep Time Digital Earth Series Symposiums. The authors appreciate Prof. Yupeng Yao and Chaolin Zhao from the Geoscience Department of NSFC for their guidance on the overall design and research directions in the original exploration project of NSFC. Many thanks are also given to the anonymous peer reviewers for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (Grant Nos. 41421001, 42050101, and 42050105).

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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

    Chenghu Zhou, Hua Wang & Yunqiang Zhu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Chenghu Zhou, Hua Wang & Yunqiang Zhu

  3. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, 100083, China

    Chengshan Wang & Qiuming Cheng

  4. Key Laboratory of Deep-Earth Dynamics of Ministry of Natural Resources, Institute of Geology, Chinese Academy of Geological Sciences, Beijing, 100037, China

    Zengqian Hou

  5. Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China

    Zhiming Zheng

  6. State Key Laboratory for Mineral Deposits Research, School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China

    Shuzhong Shen, Junxuan Fan & Xiumian Hu

  7. Sinopec Petroleum Exploration & Production Research Institute, Beijing, 100083, China

    Zhiqiang Feng

  8. School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xinbing Wang

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

    Hairong Lv

  10. Institute of Sedimentary Geology, Chengdu University of Technology, Chengdu, 610059, China

    Mingcai Hou

Authors
  1. Chenghu Zhou
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  2. Hua Wang
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  3. Chengshan Wang
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  9. Xinbing Wang
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  10. Hairong Lv
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  11. Junxuan Fan
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  13. Mingcai Hou
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  14. Yunqiang Zhu
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Corresponding authors

Correspondence to Chenghu Zhou or Hua Wang.

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Zhou, C., Wang, H., Wang, C. et al. Geoscience knowledge graph in the big data era. Sci. China Earth Sci. 64, 1105–1114 (2021). https://doi.org/10.1007/s11430-020-9750-4

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  • DOI: https://doi.org/10.1007/s11430-020-9750-4

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