Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
This work addresses the problem of predicting missing facts in temporal knowledge graphs for applications like recommendation systems, though it appears incremental as it builds on existing quaternion-based methods.
The paper tackles temporal knowledge graph completion by introducing quaternion representations in hypercomplex space to capture time-sensitive relations, achieving state-of-the-art performance on public datasets.
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.