  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.
