Geoscience knowledge graph in the big data era

Geoscience knowledge graph in the big data era

Big data

Digital storage

Geology

Knowledge graph

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. 2021, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.

1105-1114

7

64

Zhou, Chenghu

Wang, Hua

Wang, Chengshan

Hou, Zengqian

Zheng, Zhiming

Shen, Shuzhong

Cheng, Qiuming

Feng, Zhiqiang

Wang, Xinbing

Lv, Hairong

Fan, Junxuan

Hu, Xiumian

Hou, Mingcai

Zhu, Yunqiang

Science China Earth Sciences

2021

journalArticle

16747313

10.1007/s11430-020-9750-4

http://dx.doi.org/10.1007/s11430-020-9750-4