An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Geoscience knowledge graph (GKG) can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services. As a result, it has gained significant attention and become a frontier in geoscience. Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales, granularities, and dimensions. Therefore, establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG. However, existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships. To address this issue, this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge. On this basis, an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships. Under the constraint of a unified spatiotemporal ontology, this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation. This model can efficiently represent geoscience knowledge, thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval. It can further enable the alignment, transformation, computation, and reasoning of spatiotemporal information through a spatiotemporal ontology.