Knowledge Graph (KG) completion has been excessively studied with a massive number of models proposed for the Link Prediction (LP) task. The main limitation of such models is their insensitivity to time. Indeed, the temporal aspect of stored facts is often ignored. To this end, more and more works consider time as a parameter to complete KGs. In this paper, we first demonstrate that, by simply increasing the number of negative samples, the recent AttH model can achieve competitive or even better performance than the state-of-the-art on Temporal KGs (TKGs), albeit its nontemporality. We further propose Hercules, a time-aware extension of AttH model, which defines the curvature of a Riemannian manifold as the product of both relation and time. Our experiments show that both Hercules and AttH achieve competitive or new state-of-the-art performances on ICEWS04 and ICEWS05-15 datasets. Therefore, one should raise awareness when learning TKGs representations to identify whether time truly boosts performances.

在 Knowledge Graph completion 中，本文提出增加负例样本数量的方法被证明可以让不考虑时间因素的 AttH 模型在 Temporal KGs 上取得比现有最优解甚至更好的表现，同时提出了考虑时间因素的 Hercules 模型，取得了竞争性或全新最优的表现。因此，在学习 Temporal KGs 时，需要注意时间因素是否真正提高了性能表现。

具有关系和时间曲率的双曲时态知识图嵌入