Process mining has emerged as a critical practice for understanding business processes through data-driven analysis. Practitioners necessarily apply diverse process and domain knowledge to guide their analyses. However, these practices are often ad-hoc and informal, failing to formalize the process knowledge being applied. While various formal methods including temporal logic have been applied to process mining, they fail to acknowledge fundamental ontological commitments of processes. Formal ontology for processes, on the other hand, are difficult to integrate into a process mining pipeline, lacking a data-driven grounding. We thus introduce process meaning patterns as a formal declarative framework to capture process knowledge being applied in process mining, based on first order logic ontology patterns. We demonstrate our framework’s ability to semi-automatically infer process knowledge motivated by real applications from Volvo IT Belgium. This paper also accompanies a preliminary implementation of the framework using Python and Datalog.