AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query model

• Published in: International journal of digital earth Vol. 16; no. 2; pp. 4934 - 4957
• Main Authors: Han, Bing, Qu, Tengteng, Tong, Xiaochong, Wang, Haipeng, Liu, Hao, Huo, Yuhao, Cheng, Chengqi
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 08-12-2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Completeness; Computation; geo-hidden layer; Geographic knowledge graph; geographic knowledge representation; Graph representations; Graph theory; Graphical representations; Graphs; Knowledge representation; Queries; Questions; spatio-temporal query; Spatiotemporal data; time slice
• ISSN: 1753-8947; 1753-8955
• DOI: 10.1080/17538947.2023.2290569

As an emerging knowledge representation model in the domain of knowledge graphs, geographic knowledge graph can take full advantage of semantic, spatial and temporal information to facilitate answering spatio-temporal questions and completing relations. However, the representation of geographic knowledge graphs still has issues such as the difficulty of unified heterogeneous spatio-temporal data modelling, weak ability to answer spatio-temporal queries for dynamic multiobjective problems, and low efficiency of graph querying. This paper presents a grid-augmented geographic knowledge graph (AugGKG) based on the GeoSOT global subdivision grid model and time slice subgraph architecture. AugGKG discretely normalizes the spatio-temporal data of the graph, which involves five types of nodes and two types of relations. By using the geo-hidden layer of the graph and geocoding algebraic operations, the AugGKG can quickly answer complex multiobjective spatio-temporal queries and complete implicit spatio-temporal relations. Compared with existing geographic knowledge graphs (YAGO, GeoKG and GEKG), the comparative experiments verified the obvious advantages of AugGKG in terms of uniformity of accuracy, completeness, and efficiency. Hence, AugGKG is expected to be regarded as an innovative and robust geographic knowledge graph that can perform fast computation and relation completion for complex spatio-temporal queries in future geospatial question answering applications.