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.

翻译：时间知识图谱（TKG）表示学习通过整合时间信息，将实体和事件类型嵌入到连续的低维向量空间中，这对于事件预测和问答等下游任务至关重要。现有方法堆叠多个图卷积层以建模远距离实体的影响，导致过平滑问题。为缓解该问题，近期研究引入强化学习以获取有助于建模远距离实体影响的路径。然而，由于跳数限制，这些方法无法捕捉相距甚远甚至不可达实体之间的相关性。为此，我们提出GTRL，一种实体组感知的时间知识图谱表示学习方法。GTRL是首个通过仅堆叠有限层数、引入实体组建模来捕捉实体间相关性的工作。具体而言，我们提出实体组映射器，以学习方式从实体生成实体组。基于实体组，引入隐式相关性编码器以捕捉任意成对实体组之间的隐式相关性。此外，利用层次化图卷积网络在实体组图和实体图上完成消息聚合与表示更新。最后，采用门控循环单元捕捉TKG中的时间依赖性。在三个真实世界数据集上的广泛实验表明，GTRL在事件预测任务上达到最先进性能，在MRR、Hits@1、Hits@3和Hits@10上分别平均超越最佳基线13.44%、9.65%、12.15%和15.12%。