In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer future events based on historical information. However, these methods do not comprehensively consider the latent patterns behind temporal changes, to pass historical information selectively, update representations appropriately and predict events accurately. In this paper, we propose the Historical Information Passing (HIP) network to predict future events. HIP network passes information from temporal, structural and repetitive perspectives, which are used to model the temporal evolution of events, the interactions of events at the same time step, and the known events respectively. In particular, our method considers the updating of relation representations and adopts three scoring functions corresponding to the above dimensions. Experimental results on five benchmark datasets show the superiority of HIP network, and the significant improvements on Hits@1 prove that our method can more accurately predict what is going to happen.

翻译：近年来，时序知识图谱（TKG）推理受到广泛关注。现有方法大多假设训练过程中所有时间戳及其对应图结构均可用，这使得预测未来事件变得困难。为解决该问题，近期研究致力于基于历史信息推断未来事件。然而，这些方法未能全面考虑时序变化背后的潜在模式，难以实现历史信息的选择性传递、表示的恰当更新以及事件的准确预测。本文提出历史信息传递（HIP）网络用于预测未来事件。HIP网络从时序、结构和重复三个维度传递信息，分别用于建模事件的时间演化、同一时间步内事件的相互作用以及已知事件。特别地，本方法考虑了关系表示的更新，并针对上述三个维度设计了相应的评分函数。在五个基准数据集上的实验结果表明，HIP网络具有优越性，且Hits@1指标的显著提升证明本方法能更准确地预测即将发生的事件。