  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. Extensive experiments show that the proposed model
achieves substantial improvements against the state-of-the-art baselines.
