First Order Rewritability in Ontology-Mediated Querying in Horn Description Logics
Bibliographic record
Abstract
We consider first-order (FO) rewritability for query answering in ontology mediated querying (OMQ) in which ontologies are formulated in Horn fragments of description logics (DLs). In general, OMQ approaches for such logics rely on non-FO rewriting of the query and/or on non-FO completion of the data, called a ABox. Specifically, we consider the problem of FO rewritability in terms of Beth definability, and show how Craig interpolation can then be used to effectively construct the rewritings, when they exist, from the Clark’s completion of Datalog-like programs encoding a given DL TBox and optionally a query. We show how this approach to FO rewritability can also be used to (a) capture integrity constraints commonly available in backend relational data sources, (b) capture constraints inherent in mapping such sources to an ABox , and (c) can be used an alternative to deriving so-called perfect rewritings of queries in the case of DL-Lite ontologies.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".