Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective --- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.
翻译：知识嵌入图旨在学习实体和关系分布式的表达方式,并且在许多应用中证明是有效的。交叉互动 -- -- 实体和关系之间的双向效应 -- -- 在预测新的三重数据时帮助选择相关信息,但之前还没有正式讨论过。在本文中,我们建议CrossE,这是一个新的知识嵌入图,明确模拟交叉互动。它不仅像以前大多数方法一样,为每个实体了解一个普通嵌入方式和关系,而且为它们产生多个三重特定嵌入,称为互动嵌入。我们评估典型链接预测任务中的嵌入,发现CrossE在复杂和更具挑战性的数据集中取得了最新结果。此外,我们从新角度评价嵌入 -- -- 解释预测的三重,这对真正的应用很重要。在这项工作中,三重被视作头和尾实体之间的可靠封闭路径。与其他基线相比,我们实验显示CrossE从互动嵌入中受益,更有能力产生可靠解释来支持其预测。