TransG : A Generative Mixture Model for Knowledge Graph Embedding
This addresses a new issue in knowledge graph embedding for AI applications, offering a novel approach to handle multiple relation meanings.
The paper tackles the problem of multiple relation semantics in knowledge graph embedding by proposing TransG, a generative Gaussian mixture model that discovers latent semantics and uses mixture vectors for fact triples, achieving substantial improvements over state-of-the-art baselines.
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.