2020/09/26 by Dai Quoc Nguyen, Nguyen, Dai Quoc, Thanh Vu +5
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2009.12517
openalex publication_date 2020/09/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.