Knowledge graph construction and application in geosciences: A review

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 geoscience 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.

Automated summary

This paper presents a comprehensive review of knowledge graph construction and implementation in geosciences. It covers concepts and approaches relevant to knowledge graph, its application in data collection, curation, and analysis, as well as the challenges and trends in its creation and application in the near future. The review aims to be valuable to practitioners in data-intensive geoscience studies as artificial intelligence and data science continue to thrive in the field.