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Publication

Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis

Martin Atzmueller; Tim Bohne; Anne-Kathrin Patricia Windler
In: Pascal Hitzler; Abhilekha Dalal; Mohammad Saeid Mahdavinejad; Sanaz Saki Norouzi (Hrsg.). Handbook on Neurosymbolic AI and Knowledge Graphs. Pages 1-25, ISBN 9781643685786, IOS Press, 3/2025.

Abstract

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.