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.

Integrated representation of geospatial data,model,and knowledge for digital twin railway

The real-time accurate description of all spatial features of railway and their spatiotemporal relationships is a crucial factor in realizing comprehensive management and related decision-making within the entire life cycle of railways.Nevertheless,available spatiotemporal data models mainly use static historical sequence data,which are insufficient to support multi-source heterogeneous real-time sensed data;they lack a systematic depiction of the interactive relationships among multiple feature entities,and are limited to low-level descriptive analysis.Therefore,this study proposes a data-model-knowledge integrated representation data model for a digital twin railway,which explicitly describes the spatiotemporal,and interaction relationships among railway features through a conceptual knowledge graph.This study first analyzes the characteristics of railway features from above ground to underground,and then constructs a conceptual model to clearly describe the complex relationships among railway features.Secondly,a logical model is developed to illustrate the basic data structure.Thirdly,an ontology model is constructed as a basic framework for further deepening the domain knowledge graph.Finally,considering the prevention of landslides as an example,it demonstrates the abundant spatiotemporal relationships among railway related features.The results of this study bring more clear understanding of the complex interactive relationships of railway entities.