Ontologies and the Semantic Web: Problems and Perspectives for LIS Professionals
Bibliographic record
Abstract
En la actualidad, todavía hay profesionales del ámbito de la Biblioteconomía y Documentación que no conocen su papel en el desarrollo de la Web Semántica, en especial en lo concerniente a las ontologías. Este trabajo pretende contribuir a aclarar este asunto de dos maneras: en primer lugar, identificando las principales tendencias, temas y problemas tratados en la investigación sobre ontologías y, en segundo lugar, identificando las posibles contribuciones del área de la Biblioteconomía y Documentación al desarrollo de ontologías para la Web Semántica. Para ello, se presenta en primer lugar una revisión de literatura basada en búsquedas en bases de datos de cobertura internacional (LISA, SCI, SSCI, ACM Digital Library e IEEE Explore). A continuación, y a partir de dicha revisión, se presenta una discusión de las principales tendencias, temas y problemas identificados. Finalmente, se presentan recomendaciones sobre posibles contribuciones del área documental al desarrollo de ontologías para la Web Semántica.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.025 | 0.054 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".