Neuro-symbolic predictive process monitoring aims to leverage symbolic knowledge about processes to improve predictive models developed from event logs. While previous work has primarily focused on crisp declarative process knowledge, many existing neuro-symbolic formulations rely on deterministic background knowledge, where rules and constraints are assumed to hold universally and without exceptions. In real-world business processes, however, this assumption is often unrealistic, as constraints and domain rules may hold only with varying degrees of compliance. Incorporating probabilistic background knowledge therefore provides a more flexible and realistic framework for modeling domain constraints that hold with certain likelihoods rather than as strict deterministic rules. In this paper, we propose an approach that leverages a probabilistic Declare model to represent contextual factors that can improve the accuracy of trace suffix prediction. By extending crisp constraints with the probability with which they are satisfied in an event log, a probabilistic Declare model entails a set of possible compliance scenarios whose satisfaction or violation can be monitored as a trace execution unfolds. The proposed approach exploits this compliance-checking information to adjust the posterior probability of an activity occurrence returned by a next-activity predictor when generating the suffix of a given prefix. The experimental evaluation shows that incorporating this type of symbolic process knowledge generally improves the predictive performance of the model.
Alman et al. (Mon,) studied this question.