A User and NLP-Assisted Strategic Workflow for a Social Semantic OWL 2-Based Knowledge Platform
Bibliographic record
Abstract
Originating from a multidisciplinary research project that gathers, around the Semantic Web standards and principles, Social Networking and Natural Language Processing along with some Bioinformatics notions, this paper sheds the light on some of the most critical aspects of the correspondingly adopted framework and realtime knowledge architecture and modeling platform. It recognizes the considerable profits of an appropriate fusion between the aforementioned disciplines, especially via the proper exploitation of OWL 2 (Web Ontology Language) features and novelties, typically OWL 2 language profiles. Accordingly, it proposes a distinctive workflow with well-defined strategies for an ontology-aware user and NLP-assisted flexible and multidimensional approach for the management of the abundantly available Social data. Application scenarios related to awareness and orientation recommender systems based on biomedical domain ontologies for childhood obesity prevention and surveillance are explored as typical proof of concept application areas. 1
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".