Prospects for the research on geoscience knowledge graph in the Big Data Era

doi:10.1007/s11430-020-9750-4

	Prospects for the research on geoscience knowledge graph in the Big Data Era
	Zhou, Chenghu 2,3; Wang, Hua 2,3; Wang, Chengshan 4; Hou, Zengqian 5; Zheng, Zhiming 6; Shen, Shuzhong 7; Cheng, Qiuming 4; Feng, Zhiqiang 1; Wang, Xinbing 8; Lv, Hairong 9
刊名	SCIENCE CHINA-EARTH SCIENCES
	2021-05-25
页码	11
关键词	Geoscience knowledge graph All-domain geoscience knowledge representation model Federated crowd intelligence collaboration High-precision geological time scale
ISSN号	1674-7313
DOI	10.1007/s11430-020-9750-4
通讯作者	Zhou, Chenghu(zhouch@lreis.ac.cn) ; Wang, Hua(wangh@lreis.ac.cn)
英文摘要	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, 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 geoscientists. 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, and 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 intersect, and realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.
资助项目	National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[42050101] ; National Natural Science Foundation of China[42050105]
WOS关键词	GLOBAL STRATOTYPE SECTION ; CHRONOSTRATIGRAPHY ; POINT
WOS研究方向	Geology
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000654827800002
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/163939]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhou, Chenghu; Wang, Hua
作者单位	1.Sinopec Petr Explorat & Prod Res Inst, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China 5.Chinese Acad Geol Sci, Inst Geol, Minist Nat Resources, Key Lab Deep Earth Dynam, Beijing 100037, Peoples R China 6.Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China 7.Nanjing Univ, Sch Earth Sci & Engn, State Key Lab Mineral Deposits Res, Nanjing 210023, Peoples R China 8.Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China 9.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 10.Chengdu Univ Technol, Inst Sedimentary Geol, Chengdu 610059, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Chenghu,Wang, Hua,Wang, Chengshan,et al. Prospects for the research on geoscience knowledge graph in the Big Data Era[J]. SCIENCE CHINA-EARTH SCIENCES,2021:11.
APA	Zhou, Chenghu.,Wang, Hua.,Wang, Chengshan.,Hou, Zengqian.,Zheng, Zhiming.,...&Zhu, Yunqiang.(2021).Prospects for the research on geoscience knowledge graph in the Big Data Era.SCIENCE CHINA-EARTH SCIENCES,11.
MLA	Zhou, Chenghu,et al."Prospects for the research on geoscience knowledge graph in the Big Data Era".SCIENCE CHINA-EARTH SCIENCES (2021):11.