Reification Approach Enables Zero-Shot Link Prediction With Standard GNNs

A new arXiv paper proposes moving the transfer mechanism used by knowledge graph foundation models out of specialized architectures and into the data representation itself. The authors treat reification as a transferable vocabulary, allowing plain graph neural networks to perform zero-shot link prediction on previously unseen knowledge graphs. This approach aims to match dedicated models such as ULTRA without requiring architecture-level hard-coding of transfer behavior.