A machine learning approach for optimizing heuristic decision‐making in Web Ontology Language reasoners
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Description logics (DLs) are formalisms for representing knowledge bases of application domains. The Web Ontology Language (OWL) is a syntactic variant of a very expressive DL. OWL reasoners can infer implied information from OWL ontologies. The performance of OWL reasoners can be severely affected by situations that require decision‐making over many alternatives. Such a nondeterministic behavior is often controlled by heuristics that are based on insufficient information. This article proposes a novel OWL reasoning approach that applies machine learning (ML) to implement pragmatic and optimal decision‐making strategies in such situations. Disjunctions occurring in ontologies are one source of nondeterministic actions in reasoners. We propose two ML‐based approaches to reduce the nondeterminism caused by dealing with disjunctions. The first approach is restricted to propositional DL while the second one can deal with standard DL. Both approaches speed up our ML‐based reasoner by up to two orders of magnitude in comparison to the non‐ML reasoner. Another source of nondeterministic actions is the order in which tableau rules should be applied. On average, our ML‐based approach achieves a speedup of two orders of magnitude when compared to the most expensive rule ordering of the non‐ML reasoner.
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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 | 0.001 |
| 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 | 0.001 | 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