Monitoring and Mitigating Calibration Drift in Continuously Updated Evidence-Graded Knowledge Graphs

Zenodo (CERN European Organization for Nuclear Research) 2026 Preprint Open access

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Authors: Rogier Meulenaar
DOI: 10.5281/zenodo.19026299
Cited by: 0

Abstract

Paper 13 in the Prioris research series. Addresses calibration drift in continuously updated knowledge graphs. Evaluates CUSUM, EWMA, and KS-test detection methods across four drift scenarios. Compares Platt re-fit, isotonic regression, and Bayesian update recalibration strategies. Part of the Evidence-Graded Knowledge Graphs research programme (Paper 0: 10.5281/zenodo.19024611).

Fields of Study & Subjects

Data Stream Mining Techniques Advanced Graph Neural Networks Adversarial Robustness in Machine Learning

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 March 2026
Language
EN
Document type
Preprint