QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909584512253952 |
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| author | Fazael-Ardakani, Hamideh-Sadat Soltanian-Zadeh, Hamid |
| author_facet | Fazael-Ardakani, Hamideh-Sadat Soltanian-Zadeh, Hamid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding Fazael-Ardakani, Hamideh-Sadat Soltanian-Zadeh, Hamid Machine Learning 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. |
| title | QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.13983 |