TransG : A Generative Model for Knowledge Graph Embedding
Han Xiao, Minlie Huang, Xiaoyan Zhu · 2016
Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence.This paper proposes a novel generative model (TransG) to address the issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples.The new model can discover latent semantics for a relation and leverage a mixture of relationspecific component vectors to embed a fact triple.To the best of our knowledge, this is the first generative model for knowledge graph embedding, and at the first time, the issue of multiple relation semantics is formally discussed.Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.