Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development 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 prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and are not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically, we first construct UrbanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such as administrative boroughs, POIs, and road segments. Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such as hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into 9 spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of knowledge-enhanced USTP models under different task settings but also highlight the potential of state-of-the-art high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at https://github.com/usail-hkust/UUKG/.

翻译：精准的城市时空预测对智慧城市的发展与运行至关重要。作为一种新兴的基础构建模块，多源城市数据通常被整合为城市知识图谱，为城市时空预测模型提供关键知识。然而，现有城市知识图谱往往针对特定下游预测任务定制，且未公开，这限制了其潜在发展。本文提出UUKG，即面向知识增强型城市时空预测的统一城市知识图谱数据集。具体而言，我们首先通过连接行政区域、兴趣点、道路段等异构城市实体，为两个大都市构建了包含数百万三元组的城市知识图谱。此外，我们对所构建的城市知识图谱进行了定性与定量分析，揭示了层次结构、循环结构等多样化的高阶结构模式，这些模式可被用于提升下游城市时空预测任务。为验证并促进城市知识图谱的应用，我们在知识图谱补全任务上实现并评估了15种知识图谱嵌入方法，并将学习到的知识图谱嵌入集成到9个时空模型中，用于五项不同的城市时空预测任务。大量实验不仅提供了不同任务设置下知识增强型时空预测模型的基准，还凸显了现有最先进的高阶结构感知城市知识图谱嵌入方法的潜力。我们希望所提出的UUKG能够推动城市知识图谱及广泛智慧城市应用的研究。数据集与源代码已发布于https://github.com/usail-hkust/UUKG/。