Refactoring of Ontologies: Improving the Design of Ontological Models with Concept Analysis
Bibliographic record
Abstract
It is now widely accepted that in order to optimize both their usage and their design and maintenance ontologies should comply to design quality criteria, e.g., absence of redundancies and appropriate level of abstraction. Yet given the variety and scope of activities comprised in the life-cycle of an ontological model (OM), such as adapting, splitting, populating, this quality is easily compromised, especially with ontologies of larger size and/or resulting from the merge of smaller ones. Conversely, restoring it through refactoring, i.e., restructuring of the ontology to improve defects, is knowingly a challenging task as relocating an ontology element can adversely affect its neighbors. We investigate here a holistic refactoring approach that, given an ontology, amounts to presenting its designer with a list of the most plausible abstract entities missing in it. The core of the approach is a recently devised concept analysis method, called 'relational', that allows deeper refactoring by feeding into the process various ontological relations, e.g., concept-to-property incidences. The focus here is put on the NLP-aspects of the refactoring, while we also provide some preliminary results from a series of validating experiments.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".