TransG : A Generative Mixture Model for Knowledge Graph Embedding

TL;DR

TransG introduces a generative Gaussian mixture model for knowledge graph embedding that effectively captures multiple relation semantics, leading to significant improvements over existing methods.

Contribution

It is the first generative model for knowledge graph embedding that handles multiple relation semantics using a mixture of relation components.

Findings

Achieves substantial performance improvements over state-of-the-art baselines.

Effectively discovers latent relation semantics.

Demonstrates the effectiveness of a generative mixture model in knowledge graph embedding.

Abstract

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.