Intégration de services de raisonnement automatique basés sur les logiques de\ndescription dans les applications dâentreprise
Bibliographic record
Abstract
Ce mémoire présente un patron d’architecture permettant, dans un contexte orientéobjet,\nl’exploitation d’objets appartenant simultanément à plusieurs hiérarchies fonctionnelles.\nCe patron utilise un reasoner basé sur les logiques de description (web sémantique)\npour procéder à la classification des objets dans les hiérarchies. La création des\nobjets est simplifiée par l’utilisation d’un ORM (Object Relational Mapper). Ce patron\npermet l’utilisation effective du raisonnement automatique dans un contexte d’applications\nd’entreprise.\nLes concepts requis pour la compréhension du patron et des outils sont présentés. Les\nconditions d’utilisation du patron sont discutées ainsi que certaines pistes de recherche\npour les élargir. Un prototype appliquant le patron dans un cas simple est présenté. Une\nméthodologie accompagne le patron. Finalement, d’autres utilisations potentielles des\nlogiques de description dans le même contexte sont discutées.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".