GraphOracle: A Foundation Model for Knowledge Graph Reasoning

• URL: http://arxiv.org/abs/2505.11125v1
• Date: Fri, 16 May 2025 11:14:57 GMT
• Title: GraphOracle: A Foundation Model for Knowledge Graph Reasoning
• Authors: Enjun Du, Siyi Liu, Yongqi Zhang,
• Abstract summary: We introduce textbftextscGraphOracle, a relation-centric foundation model that unifies reasoning across knowledge graphs.<n>A query-dependent attention mechanism is developed to learn inductive representations for both relations and entities.<n>Pre-training on diverse knowledge graphs, followed by minutes-level fine-tuning, enables effective generalization to unseen entities, relations, and entire graphs.
• Score: 9.894106590443714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models have demonstrated remarkable capabilities across various domains, but developing analogous models for knowledge graphs presents unique challenges due to their dynamic nature and the need for cross-domain reasoning. To address these issues, we introduce \textbf{\textsc{GraphOracle}}, a relation-centric foundation model that unifies reasoning across knowledge graphs by converting them into Relation-Dependency Graphs (RDG), explicitly encoding compositional patterns with fewer edges than prior methods. A query-dependent attention mechanism is further developed to learn inductive representations for both relations and entities. Pre-training on diverse knowledge graphs, followed by minutes-level fine-tuning, enables effective generalization to unseen entities, relations, and entire graphs. Through comprehensive experiments on 31 diverse benchmarks spanning transductive, inductive, and cross-domain settings, we demonstrate consistent state-of-the-art performance with minimal adaptation, improving the prediction performance by up to 35\% compared to the strongest baselines.

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