Knowledge-augmented learning enables the combination of knowledge-based and data-driven approaches. For anomaly detection and diagnosis, understandability is typically an important factor, especially in high-risk areas. Therefore, explainability and interpretability are also major criteria in such contexts. This chapter focuses on knowledge-augmented explainable and interpretable learning to enhance understandability, transparency and ultimately computational sensemaking. We exemplify different approaches and methods in the domains of anomaly detection and diagnosis - from comparatively simple interpretable methods towards more advanced neuro-symbolic approaches.
@article{arxiv.2412.00146,
title = {Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis},
author = {Martin Atzmueller and Tim Bohne and Patricia Windler},
journal= {arXiv preprint arXiv:2412.00146},
year = {2024}
}