TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
Xuanyu Zhang, Qing Kun Yang, Dongliang Xu · 2022
Knowledge graph embedding (KGE) aims to learn continuous vector representations of relations and entities in knowledge graph (KG).Recently, transition-based KGE methods have become popular and achieved promising performance.However, scoring patterns like TransE are not suitable for complex scenarios where the same entity pair has different relations.Although some models attempt to employ entityrelation interaction or projection to improve entity representation for one-to-many/many-toone/many-to-many complex relations, they still continue the traditional scoring pattern, where only a single relation vector in the relation part is used to translate the head entity to the tail entity or their variants.And recent research shows that entity representation only needs to consider entities and their interactions to achieve better performance.Thus, in this paper, we propose a novel transition-based method, TranS, for KGE. The single relation vector of the relation part in the traditional scoring pattern is replaced by the synthetic relation representation with entity-relation interactions to solve these issues.And the entity part still retains its independence through entity-entity interactions.Experiments on a large KG dataset, ogbl-wikikg2, show that our model achieves state-of-the-art results.