Knowledge graph construction and application in geosciences: A review
Knowledge graph construction and application in geosciences: A review
复制标题
DOI：
10.31223/x5z898
复制
发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Xiaogang Ma
中科院分区：
文献类型：
--
作者：
Xiaogang Ma
知识图（KG）是地球科学家非常感兴趣的话题，因为它可以在数据密集型地球科学研究的整个数据生命周期中部署。然而，与地球科学中机器学习应用的大量出版物相比，对地球科学知识图谱的总结和评论仍然有限。本文的目的是对地球科学领域知识图谱的构建和实施进行全面回顾。它由四个主要部分组成：1）知识图谱相关概念和知识图谱构建方法，2）知识图谱在数据收集、管理和服务中的应用，3）知识图谱在数据分析中的应用，4）近期地球科学知识图谱创建和应用的挑战和趋势。前三部分的每一部分都总结了一系列概念、范例研究和最佳实践。这些总结在挑战和趋势分析中综合在一起。随着人工智能和数据科学在地球科学领域的蓬勃发展，我们希望对地球科学知识图谱的回顾能够对数据密集型地球科学研究的从业者有价值。
Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG construction and implementation in geosciences. It consists of four major parts: 1) concepts relevant to KG and approaches for KG construction, 2) KG application in data collection, curation, and service, 3) KG application in data analysis, and 4) challenges and trends of geo-science KG creation and application in the near future. For each of the first three parts, a list of concepts, exemplar studies, and best practices are summarized. Those summaries are synthesized together in the challenge and trend analyses. As artificial intelligence and data science are thriving in geosciences, we hope this review of geoscience KGs can be of value to practitioners in data-intensive geoscience studies.