论文标题
Trans：基于过渡的知识图嵌入具有合成关系表示
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
论文作者
论文摘要
知识图嵌入（KGE）旨在学习知识图中关系和实体的连续向量。最近，基于过渡的KGE方法达到了有希望的表现，在这种情况下，单个关系向量学会将头部实体转化为尾部实体。但是，这种评分模式不适用于相同实体对具有不同关系的复杂场景。以前的模型通常着重于改进1到N，N-to-1和N-to-N关系的实体表示形式，但忽略了单个关系向量。在本文中，我们提出了一种基于过渡的新方法，用于嵌入知识图。传统评分模式中的单个关系向量被合成关系表示取代，这可以有效，有效地解决这些问题。大知识图数据集的实验OGBL-Wikikg2表明我们的模型可实现最新的结果。
Knowledge graph embedding (KGE) aims to learn continuous vectors of relations and entities in knowledge graph. Recently, transition-based KGE methods have achieved promising performance, where the single relation vector learns to translate head entity to tail entity. However, this scoring pattern is not suitable for complex scenarios where the same entity pair has different relations. Previous models usually focus on the improvement of entity representation for 1-to-N, N-to-1 and N-to-N relations, but ignore the single relation vector. In this paper, we propose a novel transition-based method, TranS, for knowledge graph embedding. The single relation vector in traditional scoring patterns is replaced with synthetic relation representation, which can solve these issues effectively and efficiently. Experiments on a large knowledge graph dataset, ogbl-wikikg2, show that our model achieves state-of-the-art results.
