COMPETENCY QUESTIONS FOR BIOMEDICAL ONTOLOGY REUSE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reusing ontologies has been recognized as a good practice that most ontology building methodologies encourage. Indeed, reuse supports the semantic interoperability among different datasets and applications, increases accuracy, and reduces engineering costs and efforts. Nevertheless, many problems arise during the process since the latter is far from being automated, and instead requires significant commitment from the knowledge engineer. Inconsistencies have to be resolved when the same concepts are differently represented in different ontologies or some parts reused have to be altered. In this paper, we present a new approach to resolve theses inconsistencies. We use competency questions to capture the scope and content of each concept that is represented differently in several ontologies. The proposed approach is applied to the pneumonia domain, specifically to the pneumonia diagnosis. We reused 9 ontologies and we resolved 47 inconsistencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it