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 relation-specific 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-theart baselines. All of the related poster, slides, datasets and codes have been published in http://www.ibookman. net/conference.html.
TransG, a generative model address the issue of multiple relation semantics of a relation, has showed to go beyond state-of-the-art results REF .
TransG : A Generative Mixture Model for Knowledge Graph Embedding
ArXiv
ArXiv
Computer Science