Temporal knowledge graph embedding can be used to improve the coverage of temporal KGs via link predictions. Most existing works only concentrate on the target facts themselves, regardless of the rich and informative interactions between the target facts and their highly-related contexts. In this paper, we propose a novel approach to take advantage of useful contextual interactions from two aspects, namely temporal consistency and contextual consistency. More specifically, temporal consistency measures how well the target fact interacts with its surrounding contexts in the temporal dimension, while contextual consistency treats all facts as a whole integrity and captures the semantic interactions between multiple contexts. Additionally, considering the existence of useless and misleading context information, we design a crafted context selection strategy to pick out the most useful contexts with reference to the target facts, and then encode them using deep neural networks to capture the temporal and semantic interactions. Experimental results on real world datasets verify the effectiveness of our proposals comparing with competitive KGE methods and temporal KGE methods.

时间知识图嵌入可用于通过链接预测来改善时间KG的覆盖范围。现有的大多数作品都只关注目标事实本身，而与目标事实及其高度相关的上下文之间的丰富而丰富的交互作用无关。在本文中，我们从时间一致性和上下文一致性两个方面提出了一种利用有用的上下文交互的新颖方法。更具体地说，时间一致性衡量目标事实在时间维度上与其周围上下文交互的程度，而上下文一致性则将所有事实视为一个整体，并捕获多个上下文之间的语义交互。此外，考虑到存在无用和误导性的上下文信息，我们设计了一种精心设计的上下文选择策略，以参考目标事实挑选出最有用的上下文，然后使用深度神经网络对它们进行编码，以捕获时间和语义交互。与竞争性KGE方法和时间性KGE方法相比，真实数据集上的实验结果证明了我们的建议的有效性。