Spatio-temporal knowledge embedding method considering the lifecycle of geographical entities

Abstract

With the emergence of substantial amounts of data featuring spatio-temporal characteristics, research on spatio-temporal knowledge has garnered widespread attention. However, the existing knowledge graph embedding methods are difficult to embed spatio-temporal knowledge in continuous time, failing to meet the unique embedding requirements of geographic entities for uninterrupted, constantly changing, and smoothly continuous embeddings. To address this issue, we begin by outlining a version-based representation of spatio-temporal knowledge. Building on this foundation, we propose a novel and effective knowledge graph embedding model called Version Embedding (VerE), which embeds continuous time into vector space, transforms geographic entities into corresponding versions using an attention mechanism, and calculates spatio-temporal knowledge scores under the constraint of version similarity regularization. Subsequently, we introduce a method for link prediction based on unknown time with the aim of assessing the model’s generalization capabilities in time representation. Finally, experiments conducted on two real-world datasets demonstrate significant performance improvement of VerE compared to most existing models, and its ability to maintain stability and continuity in link prediction under unknown time. These experiments confirmed the effectiveness of the proposed model and provided new perspectives and methods for spatio-temporal knowledge embedding.