Complete Classification of Complex ALCHO Ontologies Using a Hybrid Reasoning Approach.
Bibliographic record
Abstract
Abstract. Consequence-based reasoners are typically significantly faster than tableau-based reasoners for ontology classification. However, for more expressive DL languages like ALCHO, consequence-based reasoners are not applicable, but tableau-based reasoners can sometimes require an unacceptably long time for large and complex ontologies. This paper presents a weakening and strengthening approach for classification of ALCHO ontologies, using a hybrid of consequence- and tableau-based reasoning. We approximate the original ontology Oo by a weakened version Ow and a strengthened version Os, both are in a less expressive DL ALCH and classified by a consequence-based main reasoner. The classification from Ow is sound but possibly incomplete with respect to Oo, while that from Os is complete but possibly unsound. The additional subsumptions derived from Os may be unsound so are further verified by a tableau-based assistant reasoner. A prototype classifier called WSClassifier is implemented based on this hybrid approach. The experiments results show that for classifying many large and complex ALCHO ontologies, WSClassifier’s performance is significantly faster than tableau-based reasoners. 1
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".