论文标题
rotateQVS：代表时间信息作为时间知识图的旋转空间中的旋转
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion
论文作者
论文摘要
时间因素与现实应用中事实的增长有关，例如疾病的进步和政治状况的发展，因此，对时间知识图（TKG）的研究引起了很多关注。在TKG中，需要研究具有时间性固有的关系模式，以进行跨时间事实的表示和推理。但是，现有方法几乎无法建模时间关系模式，也无法捕获随着时间的流逝而发展时关系之间的固有联系，缺乏可解释性。在本文中，我们提出了一种新型的时间建模方法，该方法代表时间实体作为季节矢量空间中的旋转（旋转QV）和汉密尔顿季节空间中的复杂矢量的关系。我们证明我们的方法可以模拟TKG中关系的关键模式，例如对称，不对称，逆，并可以通过理论捕获随时间发展的关系。从经验上讲，我们表明我们的方法可以在四个时间知识图基准上提高链接预测任务的性能。
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.
