Dynamic Relation Learning for Link Prediction in Knowledge Hypergraphs

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).