Representation learning based on temporal knowledge graphs (TKGs) has attracted widespread interest, and TKG embeddings express time entity and relation tokens and exhibit strong dynamics. Despite the significance of the dynamics and the persistent updates in TKGs, most studies have been devoted to static knowledge graphs. Moreover, previous temporal works ignored the semantic hierarchies observed in knowledge modelling cases, which are common in real-world applications. Inaccurate semantic

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... essions caused by incomplete projections might not capture complex topological structures very well. To solve this problem, a novel hierarchical time-surface embedding (HTSE) model is proposed for the representation learning of entities, relations and time. Specifically, a unified relation-oriented hierarchical space aims to distinguish relations at different semantic levels of a hierarchy , and entities can naturally reflect the corresponding hierarchy. Then, a time surface aims to enhance the temporal characteristics, and quadruples are learned through exponential mapping and tangent planes in the time surface. According to extensive experiments, HTSE can achieve remarkable performance on five benchmark datasets, outperforming baseline models for time scope prediction, temporal link prediction and hierarchical relation embedding tasks. Furthermore, the qualitative analysis is used to demonstrate the explainable strategy for hierarchical embeddings and their significance in TKGs.