RotateQVS：基于四元数向量空间旋转的时态知识图谱完备化方法
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion
摘要：
在实际应用中，时间因素与事实的增长密切相关，如疾病的进展、政治局势的发展等，因此，时间知识图谱（TKG）的研究备受关注。在TKG中，时间性所固有的关系模式需要被研究，以便进行跨时间事实的表示学习和推理。然而，现有的方法很难建模的时间关系模式，也不能捕捉随着时间的推移，关系之间的内在联系，缺乏可解释性。在本文中，我们提出了一种新的时间建模方法，表示时间实体的旋转四元数向量空间（RotateQVS）和汉密尔顿的四元数空间的复向量的关系。我们证明了我们的方法可以模拟TKG中的关键关系模式，如对称，不对称，逆，并可以进一步捕获时间演变的关系理论。经验上，我们表明，我们的方法可以提高性能的链接预测任务超过四个时间知识图谱基准。
英文摘要：
Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention. In TKG, relation patterns inherent with temporality are required to be studied for representation learning and reasoning across temporal facts. However, existing methods can hardly model temporal relation patterns, nor can capture the intrinsic connections between relations when evolving over time, lacking of interpretability. In this paper, we propose a novel temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space (RotateQVS) and relations as complex vectors in Hamilton's quaternion space. We demonstrate our method can model key patterns of relations in TKG, such as symmetry, asymmetry, inverse, and can further capture time-evolved relations by theory. Empirically, we show that our method can boost the performance of link prediction tasks over four temporal knowledge graph benchmarks.
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。
