A Preliminary Study on Dynamic Urban Knowledge Graph Construction using Heterogeneous Spatio-temporal Data

Knowledge Graphs (KGs) have recently emerged as a powerful tool for extracting directed multi-relational "knowledge" from structured facts within massive urban mobility data, supporting various urban application scenarios. However, current methods for constructing urban knowledge graphs (UrbanKGs), which rely on static data such as points of interest (POI) and road networks, fail to capture the dynamic relations that evolve over time. In this paper, we address this limitation by constructing a dynamic urban knowledge graph using heterogeneous spatio-temporal urban data to uncover deeper "knowledge". First, spatial features and their geographical relationships are extracted as entities and relations to build the UrbanKG. Next, timestamps are integrated from regional origin-destination (OD) data to form a dynamic UrbanKG. Finally, experimental results demonstrate that the dynamic models significantly outperform the static models in the knowledge graph completion (KGC) task. This confirms that incorporating temporal dynamics into UrbanKGs provides a more robust framework for capturing the complexities of urban transportation and improving urban management strategies.