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.

翻译：超关系知识图谱（KGs）包含额外的键值对，提供了关于关系的更多信息。在许多场景中，同一关系可能对应不同的键值对，使得原始三元组事实更具区分性和特异性。先前关于超关系知识图谱的研究已建立了超关系图编码的标准化方法。本文提出一种具备全局关系结构感知能力的基于消息传递的图编码器，称为ReSaE。与先前最先进的方法相比，ReSaE在消息传递过程中强调关系间的交互，并针对链接预测任务优化了读出结构。总体而言，ReSaE为超关系知识图谱提供了编码方案，并在下游链接预测任务中确保了更强的性能。实验表明，ReSaE在多个链接预测基准上取得了最先进的性能。此外，我们还分析了不同模型结构对模型性能的影响。