Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach

Computer Science > Artificial Intelligence [Submitted on 27 Mar 2026] Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach Fabrizio De Santis, Gyunam Park, Wil M.P. van der Aalst 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 accuracy compared to baseline approaches across all compliance-aware experiments. Comments: Accepted CAiSE 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.26948 [cs.AI]   (or arXiv:2603.26948v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.26948 Focus to learn more Submission history From: Fabrizio De Santis [view email] [v1] Fri, 27 Mar 2026 19:37:13 UTC (333 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)