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

• URL: http://arxiv.org/abs/2504.13983v1
• Date: Fri, 18 Apr 2025 07:54:10 GMT
• Title: QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding
• Authors: Hamideh-Sadat Fazael-Ardakani, Hamid Soltanian-Zadeh,
• Abstract summary: QuatE-D is a novel quaternion-based model that employs a distance-based scoring function instead of traditional inner-product approaches.<n> Experimental results demonstrate that QuatE-D achieves competitive performance while maintaining an efficient parameterization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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