Dynamic relation learning for link prediction in knowledge hypergraphs

Xue Zhou, Bei Hui, Ilana Zeira, Hao Wu, Ling Tian

Abstract: Link prediction for knowledge graphs (KGs), which aims to predict missing facts, has been broadly studied in binary relational KGs. However, real world data contains a large number of high-order interaction patterns, which is difficult to describe using only binary relations. In this work, we propose a relation-based dynamic learning model RD-MPNN, based on the message passing neural network model, to learn higher-order interactions and address the link prediction problem in knowledge hypergraphs. Different from existing methods, we consider the positional information of entities within a hyper-relation to differentiate each entity’s role in the hyper-relation. Furthermore, we complete the representation learning of hyper-relations by dynamically updating hyper-relations with entity information. Extensive evaluations on two representative knowledge hypergraph datasets demonstrate that our model outperforms the state-of-the-art methods. We also compare the performance of models at differing arities (the number of entities within a relation), to show that RD-MPNN demonstrates outstanding performance metrics for complex hypergraphs (arity>2).