  Hyper-relational knowledge graphs (KGs) contain additional key-value pairs,
providing more information about the relations. In many scenarios, the same
relation can have distinct key-value pairs, making the original triple fact
more recognizable and specific. Prior studies on hyper-relational KGs have
established a solid standard method for hyper-relational graph encoding. In
this work, we propose a message-passing-based graph encoder with global
relation structure awareness ability, which we call ReSaE. Compared to the
prior state-of-the-art approach, ReSaE emphasizes the interaction of relations
during message passing process and optimizes the readout structure for link
prediction tasks. Overall, ReSaE gives a encoding solution for hyper-relational
KGs and ensures stronger performance on downstream link prediction tasks. Our
experiments demonstrate that ReSaE achieves state-of-the-art performance on
multiple link prediction benchmarks. Furthermore, we also analyze the influence
of different model structures on model performance.
