论文标题
逻辑和常识引导的时间知识图完成
Logic and Commonsense-Guided Temporal Knowledge Graph Completion
论文作者
论文摘要
时间知识图（TKG）存储从涉及时间的数据中得出的事件。由于事件的时间敏感性，预测事件极具挑战性。此外，先前的TKG完成（TKGC）方法不能同时代表事件的及时性和因果关系。为了应对这些挑战，我们提出了一种逻辑和常识性引导的嵌入模型（LCGE），以共同学习涉及及时性和事件因果关系的时间敏感表示，以及从共识的角度来看事件的时间无关。具体而言，我们设计了一种时间规则学习算法，以构建一种规则引导的谓语嵌入正规化策略，以学习事件之间的因果关系。此外，我们可以通过辅助常识知识准确地评估事件的合理性。 TKGC任务的实验结果说明了与现有方法相比，我们的模型的显着性能改进。更有趣的是，我们的模型能够根据因果推断提供预测结果的解释性。本文的源代码和数据集可在https://github.com/ngl567/lcge上找到。
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.
