Leveraging Knowledge Graphs for Earth System Dataset Discovery

Vincent Armant; Felipe Vargas-Rojas; Victoria Agazzi; Jean-Christophe Desconnets; Isabelle Mougenot; Valentina Beretta; Stephane Debard; Danai Symeonidou; Amira Mouakher; Joris Guérin; Thibault Catry; Emmanuel Roux

doi:10.1007/978-3-031-77847-6_15

Research Article
8
10.3390/min14121296

A “Pipeline”-Based Approach for Automated Construction of Geoscience Knowledge Graphs

Dec 21, 2024
Minerals
Qiurui Feng + 2 more +2

With the development of technology, Earth Science has entered a new era. Continuous research has generated a large amount of Earth Science data, including a significant amount of semi-structured and unstructured data, which contain information about locations, geographical concepts, geological characteristics of mineral deposits, and relationships. Efficient management of these Earth Science data is crucial for the development of digital earth systems, rational planning of resource industries, and resource security. By representing entities, relationships, and attributes through graph structures, knowledge graphs capture and present concepts and facts about the real world, facilitating efficient data management. However, due to the highly specialized and complex nature of Earth Science data and disciplinary differences, the methods used to construct general-purpose knowledge graphs cannot be directly applied to building knowledge graphs in the field of geological science. Therefore, this paper summarizes a “pipeline” approach to constructing an Earth Science knowledge graph in order to clarify the complete construction process and reduce barriers between data and technology. This approach divides the construction of the Earth Science knowledge graph into two parts and designs functional modules under each part to specify the construction process of the knowledge graph. In addition to proposing this approach, a knowledge graph of iron ore deposits is automatically constructed by integrating geographic and geological data related to iron ore deposits using deep learning techniques. The systematic approach presented in this paper reduces the threshold for constructing geological science knowledge graphs, provides methodological support for specific disciplines or research objects in Earth Science, and also lays the foundation for the construction of large-scale Earth Science knowledge graphs that combine crowdsourcing and expert decision-making, as well as the development of intelligent question-answering systems and intelligent decision-making systems covering the entire field of Earth Science.