用符号规则增强神经网络，让流程预测更合规更准确。
Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach
- 结合逻辑张量网络注入领域规则，实现知识驱动的预测
- 在合规性测试中准确率显著优于传统数据驱动模型
- 适合医疗等需严格遵循流程规则的场景使用
现有预测流程监控方法为非符号化，完全依赖数据学习特征间相关性，如根据历史事件和生物指标预测患者手术需求。但此类方法无法融入领域特定的流程约束（如：患者须出院超过一周才能安排手术），导致合规性差、预测不准确。本文提出一种神经符号方法，利用逻辑张量网络（LTNs）将流程知识注入预测模型。该方法包含四个阶段：特征提取、规则提取、知识库构建与知识注入。实验表明，该模型不仅能有效学习流程约束，在所有合规性测试中均表现出更高合规性与更优预测准确性，优于基线方法。
原文摘要 · Abstract (English)
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.
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