Process Representation Meets Operational Realization: An Architecture for Data-Driven Process Ontology Application Through Process Mining
Bibliographic record
Abstract
Processes are fundamental to enterprises, serving as significant engines of optimization and analysis. Understanding business processes is critical, yet integrating formal process ontologies with enterprise data and workflows remains difficult. We refer to this specific kind of ontology application, intended for practitioners working with enterprise data, as operational realization. The varied ontological commitments and highly expressive representation languages of process ontologies create barriers for operational realization, including issues of decidability, a lack of tooling, and operational constraints. This paper presents an architecture for the operational realization of process ontologies, driven by process mining needs. A key aspect of the architecture is the formalization of ontological commitments in tasks like data cleaning and analysis, which rely on implicit assumptions embedded in the interpretation of process data. Our approach builds on existing methodologies, notably ontology-based data access (OBDA), while going further by encoding domain knowledge required to interpret and reason with process data. This structure moves beyond an A-Box and T-Box distinction, explicitly capturing how the process ontology, domain data, and supporting data interpretation theories enable complex process reasoning. By structuring the dynamics of a process ontology, domain data, process data theories, and by characterizing reasoning scenarios, our approach provides a pragmatic foundation for integrating process ontologies into data-driven process workflows. We demonstrate this architecture with real enterprise data, challenge problems, and scenarios already widely used for benchmarking in process mining.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".