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.

翻译：传统的基于嵌入的模型将时序知识图谱（TKG）中的事件时间预测视为排序问题，但往往难以有效捕捉顺序、距离等关键时间关系。本文提出逻辑推理框架TEILP，将这类时间要素自然地融入知识图谱预测。我们首先将TKG转化为时间事件知识图谱（TEKG），通过图节点实现对时间的显式表示。基于TEKG，我们开发了一种可微的随机游走方法用于时间预测。最终引入与查询区间相关的逻辑规则的条件概率密度函数，据此得出时间预测结果。我们在五个基准数据集上与现有最优方法进行比较，结果表明TEILP在显著提升基线模型性能的同时，还能提供可解释的推理过程。特别地，我们考察了训练样本有限、事件类型分布不均、以及仅凭历史事件预测未来事件发生时间等多种场景，在这些情况下TEILP的鲁棒性均优于现有最优方法。