KGPFN: הפעלת הפוטנציאל של מודל הבסיס של רשת הידע דרך למידה במצב הקונטקסט

תקציר מקורי באנגליתarXiv: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 instances. We propose KGPFN, a KG foundation model built on a Prior-Data Fitted Network (PFN) that combines transferable relational representations with inference-time in-context learning over structu