Neuro-Symbolic Process Anomaly Detection
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 symbo...
| Authors | Devashish Gaikwad, Wil M. P. van der Aalst, Gyunam Park |
|---|---|
| Primary category | cs.LG |
| Published | 27 March 2026 |
| Last updated at source | 1 April 2026 |
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