Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models
arXiv:2606.18001v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining. However, understanding when and how they can robustly generalize across KGs is still an open question. In this paper, we shed some light on their generalization mechanisms highlighting how their performance on unseen KGs is not uniform when it comes to partially seen links, which we call half-links. In fact, we show that to predict a test triple $(h,r,t)$ it might suffice in practice to have observed
This paper explores the generalization capabilities of Knowledge Graph Foundation Models (KGFMs), a relatively new area gaining significant attention within AI research.
Understanding the generalization mechanisms and limitations of KGFMs is crucial for their reliable deployment in real-world applications requiring zero-shot learning on diverse knowledge graphs.
This research refines our understanding of how KGFMs generalize, particularly concerning 'half-links,' suggesting that full link observation may not always be necessary for accurate prediction.
- · AI researchers
- · Data scientists
- · Companies using knowledge graphs
Improved efficiency and accuracy in knowledge graph completion tasks using KGFMs.
Reduced data requirements for training and deploying AI systems leveraging knowledge graphs, potentially accelerating their adoption.
Enhanced automation in data integration and reasoning across various domains, leading to more profound insights from complex datasets.
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Read at arXiv cs.LG