QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
This work addresses knowledge graph completion, a key task in AI for applications like recommendation systems, but it appears incremental as it builds on existing quaternion-based methods.
The authors tackled the problem of knowledge graph completion by proposing QuatRE, a model that uses quaternion embeddings and relation-aware rotations to enhance correlations between entities. The model achieved state-of-the-art performance on benchmark datasets.
We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. The model achieves this goal by further associating each relation with two relation-aware rotations, which are used to rotate quaternion embeddings of the head and tail entities, respectively. Experimental results show that our proposed model produces state-of-the-art performances on well-known benchmark datasets for knowledge graph completion. Our code is available at: \url{https://github.com/daiquocnguyen/QuatRE}.