Construction and Updating Technology for a Spatiotemporal Evolution Knowledge Graph of Geographic Entities: Using National Fundamental Geographic Entity Data in China as Study Case

Abstract. A geographic entity is an abstract representation of a real-world geographic object or phenomenon, characterized by its location, extent, processes, interrelationships, and attributes. It serves as a fundamental unit for spatial process modelling across various domains, such as land cover change simulation, wetland management, geological monitoring, etc. Yet, current research typically emphasizes either definitional frameworks for entities or domain-specific knowledge graphs tailored to one-off analytical use cases in specific fields, thereby constraining generalizability and impeding sustained applicability in the absence of dynamic update mechanisms. To address these challenges and enable full lifecycle management and spatiotemporal knowledge discovery for geographic entities, this study introduces the Spatiotemporal Evolution Knowledge Graph (SEKG) along with its incremental update methodologies, a generalized framework built on the Property Graph model and derived from analyses of how geographic entities and their interrelationships evolve in real-world. Then, we validated the SEKG's robustness and effectiveness by implementing it based on subset of China's National Fundamental Geographic Entity Data using Neo4j and by conducting a series of comparative experiments against PostGIS-based geographic entity database. The validation demonstrates that SEKG could effectively and cohesively organize comprehensive geographic entity information, including location, extent, attributes, interrelationships, and evolution processes, while supporting incremental updates for long-term lifecycle management of geographic entities. Furthermore, as a semantic-based spatiotemporal knowledge structure, the SEKG could facilitate advanced applications such as spatiotemporal evolution simulation, semantic mining, etc. It is expected that this work may serve as a valuable reference for entity-based data management, spatiotemporal modelling, and knowledge discovery.