TransG : A Generative Mixture Model for Knowledge Graph Embedding

Understand

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 addresses a new issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples, and proposes a novel Gaussian mixture model for embedding, TransG.
• The new model can discover latent semantics for a relation and leverage a mixture of relation component vectors for embedding a fact triple.
• To the best of our knowledge, this is the first generative model for knowledge graph embedding, which is able to deal with multiple relation semantics.