KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning

arXiv:2605.14907v2 Announce Type: replace Abstract: 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

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