Ontologias bibliográficas e Web Semântica: limitações e propostas de investigação
Bibliographic record
Abstract
The literature on bibliographic data and ontologies on the Semantic Webidentifies problems, not in terms of data instances or their publicationin isolated sets, but regarding the ontologies that describe the underlying concepts, impacting on the quality of semantic interoperability and in sharing ontologies between systems.This paper elaborates on the adequacy of conceptual models and the limitations of FRBR -Functional Requirements for Bibliographic Records (IFLA, 1998, 2018)1to the Semantic Web; the absence of a common conceptual framework; the insufficiency of semantic mechanisms; the low and deficient reuse of external vocabularies; and the inadequacy of mapping methodologies being applied.A research project is presented proposing a solution to the semantic problems in sharing ontologies, through the creation of a conceptual reference model and a reference ontology as a high level mechanism for semantic relations and data validation using SHACL -Shapes Constraint Language (KNUBLAUCH e KONTOKOSTAS, 2017).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".