Recurrent Event Network for Reasoning over Temporal Knowledge Graphs.pdf下载

Recently, there has been a surge of interest in learning representation of graphstructured data that are dynamically evolving. However, current dynamic graph learning methods lack a principled way in modeling temporal, multi-relational, and concurrent interactions between nodes—a limitation that is especially problematic for the task of temporal knowledge graph reasoning, where the goal is to predict unseen entity relationships (i.e., events) over time. Here we present Recurrent Event Network (RE-NET)—an architecture for modeling complex event sequences—which consists of a recurrent event encoder and a neighborhood aggregator. TheeventencoderemploysaRNNtocapture(subject,relation)-specific patterns from historical entity interactions; while the neighborhood aggregator summarizes concurrent interactions within each time stamp. An output layer is designed for predicting forthcoming, multi-relational events. Experiments1 on temporal link prediction over two knowledge graph datasets demonstrate the effectiveness of our method, especially on multi-step inference over time.