Interpretable Relation Learning on Heterogeneous Graphs

Abstract

Relation learning, widely used in recommendation systems or relevant entity search over knowledge graphs, has attracted increasing attentions in recent years. Existing methods like network embedding and graph neural networks (GNNs), learn the node representations from neighbors and calculate the similarity score for relation prediction. Despite effective prediction performance, they lack explanations to the predicted results. We propose a novel interpretable relation learning model named IRL, which can not only predict whether relations exist between node pairs, but also make the inference more transparent and convincing. Specifically, we introduce a meta-path based path encoder to model sequential dependency between nodes through recurrent neural network. We also apply the self-supervised GNN on the extracted sub-graph to capture the graph structure by aggregating information from neighbors, which are fed into the meta-path encoder. In addition, we propose a meta-path walk pruning strategy for positive path generation and an adaptive negative sampling method for negative path generation to improve the quality of paths, which both consider the semantics of nodes in the heterogeneous graph. We conduct extensive experiments on two public heterogeneous graph data, AMiner and Delve, for different relation prediction tasks, which demonstrate significant improvements of our model over the existing embedding-based and sequential modeling-based methods.