This work introduces quaternion embeddings, hypercomplex-valued embeddings with three imaginary components, to model entities and relations for knowledge graph embeddings. Relations are modeled as rotations in the quaternion space. Experimental results show that the proposed method achieves state-of-the-art performance on knowledge graph completion benchmarks.
