用距离代替内积，提升知识图谱嵌入的可解释性与性能。
QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding
- 改用欧氏距离作为评分函数，替代传统内积方法。
- 在均秩指标上表现优异，参数量少且计算高效。
- 适合关注可解释性与结构建模的知识图谱研究者。
知识图谱嵌入（KGE）旨在连续空间中表示实体与关系，同时保留其结构和语义特性。基于四元数的KGE已展现出捕捉复杂关系模式的潜力。本文提出QuatE-D，一种新型四元数模型，采用基于距离的评分函数，而非传统的内积方法。通过利用欧氏距离，QuatE-D提升了可解释性，并提供了更灵活的关系结构表示。实验表明，该模型在保持高效参数化的同时达到具有竞争力的性能，尤其在均秩降低方面表现突出。这些发现验证了距离评分在四元数嵌入中的有效性，为知识图谱补全提供了新方向。
原文摘要 · Abstract (English)
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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