Temporal Knowledge Graph Completion with Time-sensitive Relations in
  Hypercomplex Space
Li Cai Xin Mao Zhihong Wang Shangqing Zhao Yuhao Zhou Changxu Wu Man Lan
Temporal knowledge graph completion (TKGC) aims to fill in missing facts
within a given temporal knowledge graph at a specific time. Existing methods,
operating in real or complex spaces, have demonstrated promising performance in
this task. This paper advances beyond conventional approaches by introducing
more expressive quaternion representations for TKGC within hypercomplex space.
Unlike existing quaternion-based methods, our study focuses on capturing
time-sensitive relations rather than time-aware entities. Specifically, we
model time-sensitive relations through time-aware rotation and periodic time
translation, effectively capturing complex temporal variability. Furthermore,
we theoretically demonstrate our method's capability to model symmetric,
asymmetric, inverse, compositional, and evolutionary relation patterns.
Comprehensive experiments on public datasets validate that our proposed
approach achieves state-of-the-art performance in the field of TKGC.