RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion

TL;DR

RotateQVS introduces a novel quaternion-based approach to model temporal relations in knowledge graphs, capturing key relation patterns and improving link prediction accuracy over multiple benchmarks.

Contribution

The paper presents a new method representing temporal entities as rotations in quaternion space and relations as complex vectors, enhancing interpretability and modeling of temporal relation patterns.

Findings

Successfully models symmetry, asymmetry, and inverse relation patterns.

Achieves state-of-the-art performance on four TKG benchmarks.

Captures time-evolved relations theoretically and empirically.

Abstract

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.