KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning
Knowledge graph (KG) foundation models aim to generalize to graphs with unseen entities and relations by learning transferable relational structure. Most existing methods, however, focus on relation-level universality, leaving in-context learning, the other pillar of foundation models, largely unexplored for KG reasoning. Context in KGs is structured and heterogeneous: accurate prediction requires conditioning both on the local neighborhood of the query entities and on global context that summarizes how the query relation behaves across many instances. We propose KGPFN, a KG foundation model built on a Prior-Data Fitted Network (PFN) that combines transferable relational representations w...
| Authors | Yisen Gao, Jiaxin Bai, Haoyu Huang et al. |
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| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 2 October 2026 |
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