Extrapolation over temporal knowledge graph via hyperbolic embedding

Abstract

Predicting potential facts in the future, Temporal Knowledge Graph (TKG) extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts. Intuitively, facts (events) that happened at different timestamps have different influences on future events, which can be attributed to a hierarchy among not only facts but also relevant entities. Therefore, it is crucial to pay more attention to important entities and events when forecasting the future. However, most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts, which may be affected by the diversity and variability of th..