Ontology‐based model‐driven development of a destination management portal: Experience and lessons learned
Bibliographic record
Abstract
Summary We present a case study inmodel‐driven developmentof ane‐tourism portalthat we chose to develop throughgenerationfrom adomain modelencoded as anontology. We present (1) the requirements of e‐tourism portal, which dictated its high‐level design; (2) the principles behind our implementation strategy, including the use of adomain ontologyas a starting model within the context of a model‐driven transformational approach; (3) the ontology development process and the code generation strategy used; and (4) the lessons learned. In particular, we compare our experiences to those reported in the model‐driven engineering (MDE) literature along 3 dimensions, ie, (1) the impact of MDE on the development process, (2) the choice of the modeling approach, and (3) the impact of code generation on design and code quality and testing. Overall, our experiences corroboratedsomeof thetheoretical claimsandmanyof thepractical experienceswith MDE. Key findings include (1) model‐driven development makes maintenance, not development, more efficient; (2) it does require a higher skill level than traditional development; (3) clients and managers need to be educated into whatincrementalitymeans in agenerative approach; (4) UML is neither necessary nor sufficient to handle the required representational flexibility; (5) it is difficult to build models that are good for both human consumption and code generation; and (6) it is difficult to generate code that is, simultaneously, efficient, pretty, and easy to maintain. We conclude by summarizing the findings of the paper.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".