Problem
Research questions and friction points this paper is trying to address.
predictive process monitoring
compliance
process constraints
domain knowledge
neuro-symbolic
Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach
🤖 AI Summary
This study addresses the challenge that existing predictive process monitoring approaches struggle to integrate domain-specific compliance constraints, often yielding predictions that violate business rules and suffer from limited accuracy. To bridge this gap, the work introduces neural-symbolic computing into the field for the first time, proposing a four-stage framework based on Logic Tensor Networks (LTNs). The framework encompasses feature extraction, rule extraction, knowledge base construction, and knowledge injection, explicitly embedding compliance rules to guide model learning. Experimental results demonstrate that the proposed method not only accurately captures process constraints across multiple compliance-aware tasks but also significantly outperforms current baselines in both prediction accuracy and adherence to compliance requirements, effectively reconciling data-driven learning with domain knowledge integration.
📝 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 accuracy compared to baseline approaches across all compliance-aware experiments.
Innovation
Methods, ideas, or system contributions that make the work stand out.
neuro-symbolic
predictive process monitoring
Logic Tensor Networks
compliance-aware
knowledge injection
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