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高校地质学报 ›› 2023, Vol. 29 ›› Issue (3): 382-394.DOI: 10.16108/j.issn1006-7493.2021111

• 固体地球科学文本挖掘和知识图谱专栏 特邀主编:马 超 诸云强 闾海荣 胡修棉 • 上一篇    下一篇

地球科学知识图谱比较分析与启示:构建方法与内容视角

诸云强1,2,孙 凯1*,李威蓉1,3,王 曙1,宋 佳1,2,程全英1,3,杨 杰1,牟兴林4,耿文广5,代小亮1,3
  

  1. 1. 中国科学院 地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101;
    2. 江苏省地理信息协同创新中心,南京 210023; 3. 中国科学院大学,北京 100049; 4. 自然资源部 国土卫星遥感应用中心,北京 100048 ; 5. 山东理工大学 建筑工程学院,淄博 255000
  • 出版日期:2023-06-20 发布日期:2023-06-20

Comparative Analysis and Enlightenment of Geoscience Knowledge Graphs: A Perspective of Construction Methods and Contents

ZHU Yunqiang1,2,SUN Kai1*,LI Weirong1,3,WANG Shu1,SONG Jia1,2,CHENG Quanying1,3,YANG Jie1,MU Xinglin4,GENG Wenguang5,DAI Xiaoliang1,3#br#   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 10010, China;
    2. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China;
    4. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China;
    5. School of Architecture Engineering, Shandong University of Technology, Zibo 255000, China
  • Online:2023-06-20 Published:2023-06-20

摘要: 地球科学(以下简称地学)知识图谱将地学知识以有向图的方式进行形式化表达,具有强大的知识表达能力、开放互联能力和推理预测能力,是地学与人工智能交叉融合发展的基础设施之一,已成为当前地学研究中重要的研究热点。因此,国际上很多科学组织或团队先后开展了地学领域的知识图谱研究,并构建了一系列具有代表性的知识图谱。然而,目前尚缺乏对这些知识图谱的深入研究和分析。文章从基本情况、构建方法、主要内容及特点等方面,对当前国际上主要的地学领域知识图谱进行了系统的比较分析, 并在此基础上,指出了对未来地学知识图谱研究的启示:从构建方法上,应构建地学知识图谱统一表达模型,建立融合多源、多模态数据的知识源,研究地学知识表示与计算方法;从内容上,应加强地学知识时空特征描述,考虑地学知识复杂时空关系和推理规则;从应用上,应发展地学知识质量评估和修正方法,提升地学知识图谱应用成效。

关键词: 地学知识图谱, 地学知识, 地学本体, 形式化表达, 人工智能

Abstract: Geoscience knowledge graphs (GKGs) formally represent geoscience knowledge in a way of directed graph and have strong capabilities in knowledge representation, openness and interconnectivity, and reasoning and prediction. GKGs have been one of the important infrastructures for the development of combining geoscience and artificial intelligence, thereby becoming one of the important research focuses in geoscience. Therefore, many international scientific organizations or groups have successively carried out studies in this domain, and constructed some representative GKGs. However, there is a lack of an in-depth study and analysis of these existing GKGs. To this end, this paper makes a systematic comparative analysis on their general information, construction methods, and main contents. On this basis, some enlightenments about future research of GKGs are discussed. In terms of the construction method, a unified representation framework for GKGs should be built, the source of geoscience knowledge should be enhanced by conflating multi-source and multimodal data, and methods for the representation and computation of geoscience knowledge should be studied. Regarding the contents of GKGs, complex spatiotemporal characteristics, relations, and reasoning rules should be considered. From the perspective of application, methods for assessing quality and making correction for geoscience knowledge should be developed, and application effects of GKGs should be improved.

Key words: geoscience knowledge graphs (GKGs), geoscience knowledge, geoscience ontology, formal representation, artificial intelligence

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