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.

翻译：知识图的嵌入一直是知识基础完成的一个积极研究课题(KGC),从最初的TransE、TransH、RottateE等人逐渐改进到目前最先进的QuatE。然而,QuatE忽略了实体的多面性质和关系的复杂性,只是利用在四角空间的严格操作来捕捉对子和对子之间的相互作用,留下更好的知识表达机会,从而最终帮助KGC。在本文件中,我们提出了一个新的模型,QuatDE, 包括一个动态绘图战略,以明确捕捉该实体的各种关系模式和不同的语义信息,利用过渡矢量来调整实体通过汉密尔顿产品嵌入四角空间的矢量的位置,加强三重体要素之间的特征互动能力。实验结果显示,QuatDE在三个公认的知识图表完成基准上达到最新业绩。特别是,最低要求评价在WN18和WN18ARR上相对增加了26%,在WNDERRVA上增加了15%,这证明了总体化。