Conventional embedding-based models approach event time prediction in
temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall
short in capturing essential temporal relationships such as order and distance.
In this paper, we propose TEILP, a logical reasoning framework that naturaly
integrates such temporal elements into knowledge graph predictions. We first
convert TKGs into a temporal event knowledge graph (TEKG) which has a more
explicit representation of time in term of nodes of the graph. The TEKG equips
us to develop a differentiable random walk approach to time prediction.
Finally, we introduce conditional probability density functions, associated
with the logical rules involving the query interval, using which we arrive at
the time prediction. We compare TEILP with state-of-the-art methods on five
benchmark datasets. We show that our model achieves a significant improvement
over baselines while providing interpretable explanations. In particular, we
consider several scenarios where training samples are limited, event types are
imbalanced, and forecasting the time of future events based on only past events
is desired. In all these cases, TEILP outperforms state-of-the-art methods in
terms of robustness.

传统的基于嵌入的模型通过排序问题来解决事件时间预测问题，然而往往不能很好地捕捉时间顺序和距离等关键的时间关系。本文提出 TEILP，一个自然地将这些时间要素融入到知识图谱预测中的逻辑推理框架。通过将时间知识图谱转换为具有更明确时间表示的时间事件知识图谱（TEKG），我们利用可微分随机游走方法进行时间预测。通过引入条件概率密度函数和与查询区间相关的逻辑规则，我们得出时间预测结果。与基准方法相比，我们在五个基准数据集上比较了 TEILP 的性能，结果表明我们的模型在提供可解释的解释的同时，显著改进了基线模型。特别是在训练样本有限、事件类型不平衡以及仅基于过去事件预测未来事件时间的几种情况下，TEILP 在健壮性方面优于基准模型。