A Class Validation Proposal of a Pedagogic Domain Ontology based on Clustering Analysis
Bibliographic record
Abstract
The knowledge bases of the Web are fundamentally organized in ontologies in order to answer queries based on semantics. The ontologies learning process comprises three fundamental steps: creation of classes and relationships, population and evaluation. In this paper the focus includes the classes creation, by introducing a class validation proposal using clustering analysis. As case of study was selected a pedagogical domain, where a corpus was semi-automatically built, from articles written in Spanish published in Social Sciences. Moreover, a dictionary containing classes, concepts and synonyms was included in the experiments. Clustering analysis allowed to verify the concepts that the experts considered as the most important for the domain. For the case of study selected, the cluster analysis step reports clusters with the same instances that the clusters defined by the experts.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".