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This paper is squarely about machine learning: a GNN method (Q-GNN) for knowledge graph completion that conditions message passing on the query entity via feature-wise linear modulation and semantic-type attention. The claimed contribution is architectural, and the evidence is benchmark results in transductive and inductive settings, so readers should check how much the gains hold across datasets and baselines. For ML practitioners, the useful idea is that entity-level context, not just the query relation, can drive evidence selection in relational reasoning.
Query Entity-Oriented Graph Neural Networks for Knowledge Graph Completion
arXiv:2606.05639v2 Announce Type: replace Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network (GNN)-based methods have achieved remarkable success by performing message passing over a local subgraph centered on the query entity. In these methods, the query relation conditions message computation and aggregation, whereas the query entity serves only as a structural anchor for subgraph extraction. The query relation thus constitutes the sole explicit signal for evidence selection, even though the relevant evidence for the same relation can differ substantially across query entities. We propose Q-GNN, which incorporates the query entity as an explicit conditioning signal. Q-GNN characterizes the query entity by its local structural context and semantic type, and thus remains applicable in the inductive setting. The structural context, encoded by a dedicated encoder, modulates each message through feature-wise linear modulation, while the semantic type guides neighborhood attention and candidate scoring. Experiments in both transductive and inductive settings demonstrate the effectiveness of the proposed method.
Abstract:Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network (GNN)-based methods have achieved remarkable success by performing message passing over a local subgraph centered on the query entity. In these methods, the query relation conditions message computation and aggregation, whereas the query entity serves only as a structural anchor for subgraph extraction. The query relation thus constitutes the sole explicit signal for evidence selection, even though the relevant evidence for the same relation can differ substantially across query entities. We propose Q-GNN, which incorporates the query entity as an explicit conditioning signal. Q-GNN characterizes the query entity by its local structural context and semantic type, and thus remains applicable in the inductive setting. The structural context, encoded by a dedicated encoder, modulates each message through feature-wise linear modulation, while the semantic type guides neighborhood attention and candidate scoring. Experiments in both transductive and inductive settings demonstrate the effectiveness of the proposed method.
Machine Learning (cs.LG)
arXiv:2606.05639 [cs.LG]
(or arXiv:2606.05639v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.05639 Focus to learn more arXiv-issued DOI via DataCite
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