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

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.

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

“…A knowledge graph, as an important branch of Artificial Intelligence (AI), has a strong capability to organize diversified data, which may provide a possible solution for the above question [26,27]. It can express the concept, entity and relationship in the world, and can represent complex knowledge following human cognition [28]. In the field of natural disasters, knowledge graphs have already been applied to geological and meteorological phenomena, such as floods, typhoons, earthquakes, landslides, etc.…”