Demonstrating a semantic approach to clarifying regulatory ambiguity in aircraft design and development using process mapping, UML, and ontological modeling
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0830.vid The design and development of an aircraft is a complex process, often taking place over 6-7 years, involving thousands of experts from many disciplines, and costing several billion dollars. Software tools have been developed to help manage this complexity, yet challenges remain with respect to their utility. Model-based approaches have been increasingly used to inform the development of software tools, in particular to facilitate an understanding between software developers and the domain experts for whom the software is being developed. Recent aircraft modeling research has identified challenges, such as interoperability, stemming from a lack of formal semantic structure. This paper discusses the current challenges faced in modeling literature, and proposes a formalized approach to semantic modeling of the aircraft design and development process. As certification is critical to aircraft design, the approach proposes using publicly available regulatory documentation to provide generic language and definitions, followed by an ontological model to develop a formal, aircraft domain-specific semantic structure prior to modeling for software-specific applications. Three modeling methods of process mapping, ontological modeling, and Unified Modeling Language (UML) are used to illustrate the advantages and limitations of a semantic approach to modeling. This paper uses the Advisory circular AC 21.101-1B Establishing the Certification Basis of Changed Aeronautical Products as a case study to illustrate the proposed approach. A formalized approach to modeling the aircraft design and development process contributes to structuring a model that adequately reflects the many entities and complex interactions that are implicit to this process. This paper concludes by identifying opportunities for further investigation of regulatory documentation to improve formal models of the aircraft design and development process.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".