具有双向旋转的距离自适应四元数知识图谱嵌入
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
AI总结：
针对现有四元数知识图谱嵌入模型语义匹配与距离评分的缺陷，提出结合双向旋转语义学习和距离自适应平移的新模型，经证明可处理复杂逻辑关系，在基准数据集上性能显著优于现有模型。
摘要：
四元数包含一个实部和三个虚部，为知识图谱学习提供了表达能力更强的超复数空间。现有四元数嵌入模型要么通过语义匹配，要么通过几何距离评分函数来衡量三元组的合理性。然而，语义匹配会降低实体的可分离性，而距离评分函数会削弱实体的语义。为解决这一问题，我们提出了一种新型四元数知识图谱嵌入模型。我们的模型将语义匹配与实体的几何距离相结合，以更好地衡量三元组的合理性。具体而言，在四元数空间中，我们对头实体执行右旋转，对尾实体执行反向旋转，以学习丰富的语义特征。随后，我们利用距离自适应平移来学习实体之间的几何距离。此外，我们提供了数学证明，以论证我们的模型能够处理复杂的逻辑关系。大量实验结果与分析表明，在知名的知识图谱补全基准数据集上，我们的模型性能显著优于以往模型。我们的代码可在https://github.com/llqy123/DaBR获取。
英文摘要：
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with entity's geometric distance to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. Then, we utilize distance adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://github.com/llqy123/DaBR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
