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 naturally 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。然而，它们往往难以捕获顺序和距离等关键时序关系。本文提出TEILP——一种逻辑推理框架，可自然地将此类时序要素整合到知识图谱预测中。我们首先将TKG转换为时序事件知识图谱（TEKG），该图谱以图节点形式更明确地表示时间。TEKG使我们能够开发一种基于可微分随机游走的时间预测方法。最后，我们引入与查询区间相关的逻辑规则所关联的条件概率密度函数，并据此得出时间预测结果。我们在五个基准数据集上将TEILP与最先进方法进行比较。结果表明，我们的模型在提供可解释性解释的同时，相比基线方法实现了显著改进。特别地，我们考虑了训练样本有限、事件类型不平衡以及仅基于过去事件预测未来事件时间等场景。在所有这些情况下，TEILP在鲁棒性方面均优于最先进方法。