论文标题
TripLere：通过三倍的关系向量嵌入知识图
TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors
论文作者
论文摘要
自Transe出来以来，基于翻译的知识图嵌入一直是知识表示学习的最重要分支之一。尽管近年来许多基于翻译的方法取得了一些进展，但表现仍然不令人满意。本文提出了一种名为Triplere的新颖知识图嵌入方法，其中有两个版本。 Triplere的第一个版本创造性地将关系向量分为三个部分。第二版利用了剩余的概念，并取得了更好的性能。此外，尝试使用nodePiece编码实体的尝试在减少参数大小并解决了可伸缩性问题方面实现了有希望的结果。实验表明，我们的方法在大规模知识图数据集上实现了最先进的性能，并在其他数据集上实现了竞争性能。
Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out. Although many translation-based approaches have achieved some progress in recent years, the performance was still unsatisfactory. This paper proposes a novel knowledge graph embedding method named TripleRE with two versions. The first version of TripleRE creatively divide the relationship vector into three parts. The second version takes advantage of the concept of residual and achieves better performance. In addition, attempts on using NodePiece to encode entities achieved promising results in reducing the parametric size, and solved the problems of scalability. Experiments show that our approach achieved state-of-the-art performance on the large-scale knowledge graph dataset, and competitive performance on other datasets.
