Neuro-Symbolic Process Anomaly Detection

arXiv (Cornell University)

Abstract

Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without requiring a predefined process model. However, since anomaly detection is a purely statistical task, these models fail to incorporate human domain knowledge. As a result, rare but conformant traces are often misclassified as anomalies due to their low frequency, which limits the effectiveness of the detection process. Recent developments in the field of neuro-symbolic AI have introduced Logic Tensor Networks (LTN) as a means to integrate symbolic knowledge into neural networks using real-valued logic. In this work, we propose a neuro-symbolic approach that integrates domain knowledge into neural anomaly detection using LTN and Declare constraints. Using autoencoder models as a foundation, we encode Declare constraints as soft logical guiderails within the learning process to distinguish between anomalous and rare but conformant behavior. Evaluations on synthetic and real-world datasets demonstrate that our approach improves F1 scores even when as few as 10 conformant traces exist, and that the choice of Declare constraint and by extension human domain knowledge significantly influences performance gains.

Authors 3

1. Devashish Gaikwad Aachen

   RWTH Aachen University · Eindhoven University of Technology

   Affiliation as printed

   Eindhoven University of Technology , Eindhoven , The Netherlands

   RWTH Aachen University , Aachen , Germany

2. Wil M. P. van der Aalst Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany

3. Gyunam Park