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

2024-02-23 06:55:04
Yonglin Jing

Abstract

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.

Abstract (translated)

超关系知识图(KGs)包含附加的关键值对,提供了关于关系更多信息。在许多场景中,相同的关系可能具有不同的关键值对,使得原始三元组更易识别和具体。关于超关系KGs的前期研究已经建立了一个可靠的超关系图编码方法。在这项工作中,我们提出了一个基于消息传递的图编码器,我们称之为ReSaE。与最先进的现有方法相比,ReSaE突出了关系在消息传递过程中互动,并优化了链路预测任务的输出结构。总体而言,ReSaE为超关系KGs提供了编码解决方案,并在下游链路预测任务中实现了更强的性能。我们的实验结果表明,ReSaE在多个链路预测基准测试中实现了最先进的性能。此外,我们还分析了不同模型结构对模型性能的影响。

URL

https://arxiv.org/abs/2402.15140

PDF

https://arxiv.org/pdf/2402.15140.pdf


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