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.

本文提出了一种基于消息传递的具有全局关系结构感知能力的图编码器ReSaE，用于超关系知识图谱的编码，并能够在下游链接预测任务中提供更强的性能。实验证明，ReSaE在多个链接预测基准上实现了最先进的性能，并分析了不同模型结构对模型性能的影响。

一种关系交互式的超关系知识图中信息传递方法