Bibliographic record
Abstract
The task of designing an ontology through reuse is difficult, and a major challenge in this effort is choosing between different ontologies that are candidates for reuse. To address this challenge, we introduce a notion of preference between ontologies and provide a definition that allows the devel oper to make a well-founded comparison across a set of ontologies, with respect to their semantic requirements. The preference between ontologies is based on an assessment of relative accuracy and precision, which are also defined here. These concepts formalize the underlying intuitions related to the different possible outcomes in the assessment of an ontology against a developer’s semantic requirements. We also present a procedure to demonstrate the viability of the definition of preference, resulting in a novel approach to the choice between ontologies for reuse; it is sufficiently well-defined such that it could provide the basis for tool support to assist in this task. By providing ontology developers with a means of effectively comparing different ontologies for reuse, this work addresses several of the key limitations for ontology reuse, as identified by the 2014 Ontology Summit Communiqué (Obrst et al., 2014, pp. 155–170).
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".