[2603.26948] Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach

Computer Science > Artificial Intelligence arXiv:2603.26948 (cs) [Submitted on 27 Mar 2026] Title:Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach Authors:Fabrizio De Santis, Gyunam Park, Wil M.P. van der Aalst View a PDF of the paper titled Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach, by Fabrizio De Santis and 2 other authors View PDF HTML (experimental) Abstract:Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical events and biometrics. However, such approaches fail to incorporate domain-specific process constraints (knowledge), e.g., surgery can only be planned if the patient was released more than a week ago, limiting the adherence to compliance and providing less accurate predictions. In this paper, we present a neuro-symbolic approach for predictive process monitoring, leveraging Logic Tensor Networks (LTNs) to inject process knowledge into predictive models. The proposed approach follows a structured pipeline consisting of four key stages: 1) feature extraction; 2) rule extraction; 3) knowledge base creation; and 4) knowledge injection. Our evaluation shows that, in addition to learning the process constraints, the neuro-symbolic model also achieves better performance, demonstrating higher compliance and improved accura...