Neuro-Symbolic Process Anomaly Detection

TL;DR

This paper introduces a neuro-symbolic method combining Logic Tensor Networks and Declare constraints to improve process anomaly detection, effectively integrating human domain knowledge into neural models.

Contribution

It presents a novel neuro-symbolic approach that encodes domain knowledge as soft logical guiderails within autoencoder models for anomaly detection.

Findings

Improves F1 scores with as few as 10 conformant traces.

Effectiveness depends on the choice of Declare constraints.

Enhances anomaly detection by integrating human domain knowledge.

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.