A Relation-Interactive Approach for Message Passing in Hyper-relational Knowledge Graphs

The author proposes ReSaE, a message-passing-based graph encoder for hyper-relational KGs, emphasizing interaction of relations and optimizing readout structure for link prediction tasks. The main thesis is that ReSaE provides an encoding solution for hyper-relational KGs, ensuring stronger performance on downstream link prediction tasks.