Modelling Functional Behavior of Event-based Systems: A Practical Knowledge-based Approach
Bibliographic record
Abstract
Functional behavior is considered to be the most basic, yet a critical notion in order to determine the characteristics of a system. However, how to reason about the functional behavior of a system in a systematic manner, is mostly limited by our cognitive processing abilities. While the UML-based behavior models can support a visual conceptualization of the functional behavior, they lack the rigorous, machine-processable reasoning capabilities. In this paper, we present a practical, knowledge-based approach to model the functional behavior that incorporates the notions of Commonsense Reasoning and Functional Reasoning over its core defining aspects. We demonstrate our approach with a detailed example, along with a set of use case scenarios. The main motivation behind this work was to develop a rigorous, logic-based approach to verify the levels of functional consistencies between cross-platform event-based systems. The focus of this paper, however, is to present the representational facility that can be utilized for the consistency validation system. While we provide a brief overview of the consistency validation system in this paper, a separate article will be dedicated for the comprehensive overview of the validation system itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".