QuatDE：用于知识图谱补全的动态四元数嵌入
QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
摘要：
知识图谱嵌入是知识库补全的一个研究热点，从最初的transE、transH、RotatE等算法到目前最新的QuatE算法，知识图谱嵌入技术得到了不断的改进。然而，QuatE忽略了实体的多面性和关系的复杂性，仅通过对四元数空间的严格运算来捕捉实体对和关系之间的交互，为更好的知识表示留下了机会，最终帮助KGC。本文提出了一种新的模型QuatDE，该模型采用动态映射策略来显式地捕捉实体的各种关系模式，分离实体的不同语义信息，并通过Hamilton积利用转移向量来调整实体嵌入向量在四元数空间中的点位置，增强了三元组元素之间的特征交互能力.实验结果表明，QuatDE在三个成熟的知识图谱完成基准测试中达到了最先进的性能。特别是，MR评估在WN18上相对增加了26%，在WN18RR上增加了15%，这证明了QuatDE的通用性。
英文摘要：
Knowledge graph embedding has been an active research topic for knowledge base completion (KGC), with progressive improvement from the initial TransE, TransH, RotatE et al to the current state-of-the-art QuatE. However, QuatE ignores the multi-faceted nature of the entity and the complexity of the relation, only using rigorous operation on quaternion space to capture the interaction between entitiy pair and relation, leaving opportunities for better knowledge representation which will finally help KGC. In this paper, we propose a novel model, QuatDE, with a dynamic mapping strategy to explicitly capture the variety of relational patterns and separate different semantic information of the entity, using transition vectors to adjust the point position of the entity embedding vectors in the quaternion space via Hamilton product, enhancing the feature interaction capability between elements of the triplet. Experiment results show QuatDE achieves state-of-the-art performance on three well-established knowledge graph completion benchmarks. In particular, the MR evaluation has relatively increased by 26% on WN18 and 15% on WN18RR, which proves the generalization of QuatDE.
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。
