Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs
Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren · 2020
Knowledge graph reasoning is a critical task in natural language processing.The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future.This paper proposes Recurrent Event Network (RE-NET), a novel autoregressive architecture for predicting future interactions.The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs.Specifically, our RE-NET employs a recurrent event encoder to encode past facts, and uses a neighborhood aggregator to model the connection of facts at the same timestamp.Future facts can then be inferred in a sequential manner based on the two modules.We evaluate our proposed method via link prediction at future times on five public datasets.Through extensive experiments, we demonstrate the strength of RE-NET, especially on multi-step inference over future timestamps, and achieve state-of-the-art performance on all five datasets 1 .