An ordered theory resolution calculus for hybrid reasoning in first-order extensions of description logic
Bibliographic record
Abstract
Systems for hybrid reasoning with first-order logic (FOL) extensions of description logic (DL) date back at least 20 years and are enjoying a renewed interest in the context of recent FOL extensions of OWL DL for the Semantic Web. However, current systems for reasoning with such languages can only handle subsets of FOL or they do not fully exploit recent advances in both FOL theorem proving and DL inference. In response, we present an ordered theory resolution calculus for hybrid reasoning in unrestricted FOL extensions of the DL SHI. This calculus permits near-seamless integration of highly optimized FOL theorem provers and DL reasoners while minimizing redundant reasoning and maintaining soundness and refutational completeness. Empirical results demonstrate the potential of this approach in comparison to the state-of-the-art FOL theorem provers Vampire, Otter, and SPASS. 1
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".