Knowledge Graphs as Structured Memory for Embedding Spaces: From Training Clusters to Explainable Inference
This work addresses the need for explainable and efficient non-parametric learning in domains like medical imaging, though it appears incremental as it builds on existing methods like kNN and Label Spreading.
The paper tackles the problem of improving embedding-based inference by introducing Graph Memory (GM), a structured non-parametric framework that uses knowledge graphs as memory, achieving competitive accuracy with better calibration and smoother decision boundaries while requiring significantly fewer samples.
We introduce Graph Memory (GM), a structured non-parametric framework that augments embedding-based inference with a compact, relational memory over region-level prototypes. Rather than treating each training instance in isolation, GM summarizes the embedding space into prototype nodes annotated with reliability indicators and connected by edges that encode geometric and contextual relations. This design unifies instance retrieval, prototype-based reasoning, and graph-based label propagation within a single inductive model that supports both efficient inference and faithful explanation. Experiments on synthetic and real datasets including breast histopathology (IDC) show that GM achieves accuracy competitive with $k$NN and Label Spreading while offering substantially better calibration and smoother decision boundaries, all with an order of magnitude fewer samples. By explicitly modeling reliability and relational structure, GM provides a principled bridge between local evidence and global consistency in non-parametric learning.