  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/.
