# QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/2009.12517 · HTML: https://mlsanity.com/paper/2009.12517/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Quaternion-Based Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2111.10536/) (2021)
- [Neural Probabilistic Logic Learning for Knowledge Graph Reasoning](https://mlsanity.com/paper/2407.03704/) (2024)
- [RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion](https://mlsanity.com/paper/2606.27967/) (2026)

## Abstract (arXiv)

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: urlhttps://github.com/daiquocnguyen/QuatRE.