Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

TLDR

Big earth data combines geoscience and information science, yet current research neglects knowledge value; GeoKG is needed to integrate data, knowledge, and models, but lacks spatiotemporal perspectives. The study proposes a human–machine cognitive framework, categorizes geoscience knowledge, and introduces a state‑process/condition‑result representation along with a multimodal acquisition workflow. GeoKG is organized by enhancing existing data models, creating spatiotemporal correlation indexes, and advancing graph completion techniques. The study details GeoKG construction, enabling synthetic analysis of big earth data, advancing knowledge engineering, and laying groundwork for intelligent geoscience.

Abstract

Abstract Big earth data is a cross‐domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state‐process’ and ‘condition‐result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience.