Rethinking Temporal Knowledge Graph Representation Learning: From Entities to Evolutionary Event-Centric Clusters

Existing research on Temporal Knowledge Graph (TKG) representation learning focuses on decomposing events into entities and relations, then employing various approaches to learn entity and relation representations in a low-dimensional vector space. However, all existing research overlooks the correlations among events, even though events are the core constituent elements of TKGs and involve heterogeneous correlations. To this end, we propose a Heterogeneous evolutionary Event cluster Aware Representation learning approach in Temporal Knowledge Graphs (HEART), which is the first event-centric approach in TKGs. Specifically, a heterogeneous event graph construction module is proposed to capture the diverse pairwise correlations between events by building co-entity and proximity heterogeneous edges. In addition, an event-aware multi-step evolutionary clustering module is proposed to capture continuous high-order correlations among events at different timestamps. Furthermore, an event cluster-aware unsupervised alignment mechanism is proposed to preserve the temporal smoothness of event clusters through cross-temporal alignment. Moreover, an event cluster-based self-supervised optimization mechanism is proposed to optimize the representations of event clusters. Experimental results on seven real-world datasets demonstrate that HEART achieves the state-of-the-art performance, outperforming the runner-up by an average of 3.89%, 8.14%, 5.97%, and 6.92% in MRR, Hits@1, Hits@3, and Hits@10, respectively.