Learning Knowledge Graph Embeddings via Generalized Hyperplanes

For knowledge graph completion, translation-based methods such as Trans(E and H) are promising, which embed knowledge graphs into continuous vector spaces and construct translation operation between head and tail entities. However, TransE and TransH still have limitations in preserving mapping properties of complex relation facts for knowledge graphs. In this paper, we propose a novel translation-based method called translation on generalized hyperplanes (TransGH), which extends TransH by defining a generalized hyperplane for entities projection. TransGH projects head and tail embeddings from a triplet into a generalized relation-specific hyperplane determined by a set of basis vectors, and then fulfills translation operation on the hyperplane. Compared with TransH, TransGH can capture more fertile interactions between entities and relations, and simultaneously has strong expression in mapping properties for knowledge graphs. Experimental results on two tasks, link prediction and triplet classification, show that TransGH can significantly outperform the state-of-the-art embedding methods.