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

• URL: http://arxiv.org/abs/2402.15140v2
• Date: Sat, 2 Mar 2024 04:59:36 GMT
• Title: A Relation-Interactive Approach for Message Passing in Hyper-relational Knowledge Graphs
• Authors: Yonglin Jing
• Abstract summary: We propose a message-passing-based graph encoder with global relation structure awareness ability, which we call ReSaE. Our experiments demonstrate that ReSaE achieves state-of-the-art performance on multiple link prediction benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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