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Inductive Link Prediction on N-ary Relational Facts via Semantic Hypergraph Reasoning by Yin, Gongzhu; Zhang, Hongli; Yang, Yuchen; Luo, Yi is a scholarly article available to read on EtoBox.

What is Inductive Link Prediction on N-ary Relational Facts via Semantic Hypergraph Reasoning about?

N-ary relational facts represent semantic correlations among more than two entities. While recent studies have developed link prediction (LP) methods to infer missing relations for knowledge graphs (KGs) containing n-ary relational facts, they are generally limited to transductive settings. Fully inductive settings, where predictions are made on previously unseen entities, remain a significant challenge. As existing methods are mainly entity embedding-based, they struggle to capture entity-independent logical rules. To fill in this gap, we propose an n-ary subgraph reasoning framework for fully inductive link prediction (ILP) on n-ary relational facts. This framework reasons over local subgraphs and has a strong inductive inference ability to capture n-ary patterns. Specifically, we introduce a novel graph structure, the n-ary semantic hypergraph, to facilitate subgraph extraction. Moreover, we develop a subgraph aggregating network, NS-HART, to effectively mine complex semantic correlations within subgraphs. Theoretically, we provide a thorough analysis from the score function optimization perspective to shed light on NS-HART's effectiveness for n-ary ILP tasks. Empirically, we co

Author

Yin, Gongzhu; Zhang, Hongli; Yang, Yuchen; Luo, Yi

Published

2025

Language

EN