Temporal Relational Context Learning for Extrapolation Reasoning on Temporal Knowledge Graphs

Extrapolation reasoning on Temporal Knowledge Graphs (TKGs) aims to predict future events from a set of historical Knowledge Graphs (KGs) in a chronological order. The temporally adjacent facts in TKGs naturally form event sequences, implying informative temporal event dependencies. Recently, many extrapolation works have been devoted to modelling these dependencies, but the task is still far from resolved because existing works primarily rely on encoding event information into entity representations to achieve this purpose, while overlooking the significant temporal event dependencies implied by relations. In this work, we aim to learn relational temporal context to explore the temporal event dependencies implicit in relations and propose a Temporal relational context-based temporal dependencies learning Network (Trend) to capture the temporal dependencies both semantically and structurally. Experimental results on benchmark datasets demonstrate the superiority of Trend.