  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.
