Enhancing semantic accuracy in geographic knowledge graph embeddings through temporal encoding
Chunju Zhang, Bing Xu, Shu Wang, Yunqiang Zhu, Chaoqun Chu, Kang Qu Zhou · International Journal of Geographical Information Systems · 2025
Geographic knowledge graphs (GKG), central to GeoAI, represent the culmination of knowledge engineering in the era of geographic big data. Knowledge Graph Embedding (KGE) transforms entities and relationships within a knowledge graph into a low-dimensional vector space, effectively capturing their semantic and structural properties. Geographic object knowledge encompasses both intrinsic features and spatiotemporal characteristics, with temporal features indicating the existence or state changes of objects. However, neglecting temporal aspects—such as order, continuity, granularity, and periodicity—during vector calculations can distort the embedding space, reducing the effectiveness of time-sensitive geographic queries, link prediction, and recommendations. This study introduced a temporal feature encoder and designed a fusion mechanism that integrated geographic objects and temporal features. Grounded in logical query tasks, this approach aims to enhance the temporal expressiveness by refining temporal embeddings, thereby improving query accuracy for time-sensitive tasks. A comparative analysis was conducted to evaluate the effects of different baseline models, temporal encoders, and temporal feature weights on the performance of geographic queries.