Extracting and Merging Contextualized Ontology Modules
Bibliographic record
Abstract
Ontology module extraction, from a large ontology, leads to the generation of a specialized knowledge model that is pertinent to specific problems. Existing ontology module extraction methods tend to either render a too generalized or a too restricted ontology module that at times does not capture the entire semantics of the source ontology. We present an ontology module extraction method that extracts a contextualized ontology module whilst extending the semantics of the extracted concepts and their relationships in the ontology module. Our approach features the following tenets (i) identifying the user-selected concepts that are pertinent for the problem-context at hand; (ii) extracting the user-selected concepts, their roles and their individuals; and (iii) extracting other concepts, roles and individuals that are structurally-connected with the user-selected concepts. We apply our ontology module extraction method in the Healthcare domain, and demonstrate (a) extraction of ontology modules from three prostate cancer pathway ontologies; and then (b) merging of extracted ontology modules to generate a comprehensive therapeutic work-flow knowledge for prostate cancer care management.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".