A syntax for rules in OWL 2
Bibliographic record
Abstract
Abstract. Being able to extend an OWL ontology with some form of rules is a feature that many ontology developers consider as very important. Nevertheless, working with rules in practice can be difficult since the tool support is not as good as for handling standard ontologies. Furthermore, the existing rule syntaxes are not very well aligned with the new OWL 2 standard. We propose, therefore, an extension to OWL 2 for representing rules, which is directly inspired by (DL Safe) SWRL rules, but uses and extends the succinct and human-readable functional-style syntax of OWL 2. We also propose an OWL/XML version of the syntax for easy XML serialization. Support for parsing such rules has been added to the new OWL API 3.0 and reasoning support is available in the two OWL 2 reasoners Pellet and HermiT. In HermiT, these rules can also be used in conjunction with description graphs. 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".