A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. Specifically, we design a temporal rule learning algorithm to construct a rule-guided predicate embedding regularization strategy for learning the causality among events. Furthermore, we could accurately evaluate the plausibility of events via auxiliary commonsense knowledge. The experimental results of TKGC task illustrate the significant performance improvements of our model compared with the existing approaches. More interestingly, our model is able to provide the explainability of the predicted results in the view of causal inference. The source code and datasets of this paper are available at https://github.com/ngl567/LCGE.

翻译：时间知识图谱（TKG）存储了涉及时间的数据所衍生的事件。由于事件具有时间敏感性，预测事件极具挑战性。此外，现有的时间知识图谱补全（TKGC）方法无法同时表示事件的时效性和因果性属性。为应对这些挑战，我们提出了一种逻辑与常识引导的嵌入模型（LCGE），该模型能够联合学习事件中涉及时效性和因果性的时间敏感表示，以及基于常识视角的事件时间无关表示。具体而言，我们设计了一种时序规则学习算法，用于构建规则引导的谓词嵌入正则化策略，以学习事件之间的因果关系。此外，我们能够通过辅助常识知识准确评估事件的合理性。TKGC任务的实验结果表明，与现有方法相比，我们的模型在性能上取得了显著提升。更有趣的是，我们的模型能够从因果推理的视角为预测结果提供可解释性。本文的源代码和数据集可在https://github.com/ngl567/LCGE获取。