  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.
