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Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to thedevelopment and operation of the smart city. As an emerging building block,multi-sourced urban data are usually integrated as urban knowledge graphs(UrbanKGs) to provide critical knowledge for urban spatiotemporal predictionmodels. However, existing UrbanKGs are often tailored for specific downstreamprediction tasks and are not publicly available, which limits the potentialadvancement. This paper presents UUKG, the unified urban knowledge graphdataset for knowledge-enhanced urban spatiotemporal predictions. Specifically,we first construct UrbanKGs consisting of millions of triplets for twometropolises by connecting heterogeneous urban entities such as administrativeboroughs, POIs, and road segments. Moreover, we conduct qualitative andquantitative analysis on constructed UrbanKGs and uncover diverse high-orderstructural patterns, such as hierarchies and cycles, that can be leveraged tobenefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs,we implement and evaluate 15 KG embedding methods on the KG completion task andintegrate the learned KG embeddings into 9 spatiotemporal models for fivedifferent USTP tasks. The extensive experimental results not only providebenchmarks of knowledge-enhanced USTP models under different task settings butalso highlight the potential of state-of-the-art high-order structure-awareUrbanKG embedding methods. We hope the proposed UUKG fosters research on urbanknowledge graphs and broad smart city applications. The dataset and source codeare available at https://github.com/usail-hkust/UUKG/.

UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction