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.

TripleRE 是一种新的知识图谱嵌入方法，其创新性地将关系向量分为三部分并利用残差概念实现更好的性能。此外，使用 NodePiece 对实体进行编码取得了良好的效果，缩小了参数大小并解决了可扩展性的问题，实验表明我们的方法达到了大规模知识图谱数据集的最先进水平，且在其他数据集上也有竞争力。