  Temporal Knowledge Graph (TKG) representation learning embeds entities and
event types into a continuous low-dimensional vector space by integrating the
temporal information, which is essential for downstream tasks, e.g., event
prediction and question answering. Existing methods stack multiple graph
convolution layers to model the influence of distant entities, leading to the
over-smoothing problem. To alleviate the problem, recent studies infuse
reinforcement learning to obtain paths that contribute to modeling the
influence of distant entities. However, due to the limited number of hops,
these studies fail to capture the correlation between entities that are far
apart and even unreachable. To this end, we propose GTRL, an entity Group-aware
Temporal knowledge graph Representation Learning method. GTRL is the first work
that incorporates the entity group modeling to capture the correlation between
entities by stacking only a finite number of layers. Specifically, the entity
group mapper is proposed to generate entity groups from entities in a learning
way. Based on entity groups, the implicit correlation encoder is introduced to
capture implicit correlations between any pairwise entity groups. In addition,
the hierarchical GCNs are exploited to accomplish the message aggregation and
representation updating on the entity group graph and the entity graph.
Finally, GRUs are employed to capture the temporal dependency in TKGs.
Extensive experiments on three real-world datasets demonstrate that GTRL
achieves the state-of-the-art performances on the event prediction task,
outperforming the best baseline by an average of 13.44%, 9.65%, 12.15%, and
15.12% in MRR, Hits@1, Hits@3, and Hits@10, respectively.
