Knowledge graph embedding (KGE) methods aim to represent entities and relations in a continuous space while preserving their structural and semantic properties. Quaternion-based KGEs have demonstrated strong potential in capturing complex relational patterns. In this work, we propose QuatE-D, a novel quaternion-based model that employs a distance-based scoring function instead of traditional inner-product approaches. By leveraging Euclidean distance, QuatE-D enhances interpretability and provides a more flexible representation of relational structures. Experimental results demonstrate that QuatE-D achieves competitive performance while maintaining an efficient parameterization, particularly excelling in Mean Rank reduction. These findings highlight the effectiveness of distance-based scoring in quaternion embeddings, offering a promising direction for knowledge graph completion.
View on arXiv@article{fazael-ardakani2025_2504.13983,
title={ QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding },
author={ Hamideh-Sadat Fazael-Ardakani and Hamid Soltanian-Zadeh },
journal={arXiv preprint arXiv:2504.13983},
year={ 2025 }
}