Knowledge bases (KBs) are far from complete, necessitating a demand for KB completion. Among various methods, embedding has received increasing attention in recent years. PTransE, an important approach using embedding method in KB completion, considers multiple-step relation paths based on TransE, but ignores the association between entity and their related entities with the same direct relationships. In this paper, we propose an approach called EP-TransE, which considers this kind of association. As a matter of fact, the dissimilarity of these related entities should be taken into consideration and it should not exceed a certain threshold. EPTransE adjusts the embedding vector of an entity by comparing it with its related entities which are connected by the same direct relationship. EPTransE further makes the euclidean distance between them less than a certain threshold. Therefore, the embedding vectors of entities are able to contain rich semantic information, which is valuable for KB completion. In experiments, we evaluated our approach on two tasks, including entity prediction and relation prediction. Experimental results show that our idea of considering the dissimilarity of related entities with the same direct relationships is effective.

中文翻译：

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与实体相关的路径建模，可完成知识库

知识库（KB）远未完成，因此需要完成KB。在各种方法中，嵌入近年来受到越来越多的关注。PTransE是在KB补全中使用嵌入方法的一种重要方法，它考虑了基于TransE的多步关系路径，但忽略了具有相同直接关系的实体及其相关实体之间的关联。在本文中，我们提出了一种称为EP-TransE的方法，该方法考虑了这种关联。实际上，应该考虑这些相关实体的相异性，并且不应超过某个阈值。EPTransE通过将实体的嵌入向量与其通过相同直接关系连接的相关实体进行比较来调整其嵌入向量。EPTransE进一步使它们之间的欧式距离小于某个阈值。因此，实体的嵌入向量能够包含丰富的语义信息，这对于KB的完成很有价值。在实验中，我们在两个任务上评估了我们的方法，包括实体预测和关系预测。实验结果表明，我们考虑具有相同直接关系的相关实体的不相似性的想法是有效的。