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arXiv · 2504.13983

QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding

Hamideh-Sadat Fazael-Ardakani·Hamid Soltanian-Zadeh
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Abstract

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.

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Hamideh-Sadat Fazael-Ardakani, Hamid Soltanian-Zadeh. 2025-04-18. QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding. https://arxiv.org/abs/2504.13983

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