Optimization techniques for retrieving resources described in OWL/RDF documents: first results
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
Practical description logic systems play an evergrowing role for knowledge representation and reasoning research even in distributed environments. In particular, the often-discussed semantic web initiative is based on description logics (DLs) and defines important challenges for current system implementations. Recently, several standards for representation languages have been proposed (RDF, OWL). By introducing optimization techniques for inference algorithms we demonstrate that sound and complete query engines for semantic web representation languages can be built for practically significant query classes. The paper introduces and evaluates optimization techniques for the instance retrieval problem w.r.t. the description logic SHIQ(Dn)-, which covers large parts of OWL. The paper discusses practical experiments with the description logic system RACER.
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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 | 0.001 |
| 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