Handling Non-determinism with Description Logics using a Fork/Join Approach
Why this work is in the frame
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Bibliographic record
Abstract
The increasing use of Ontologies, formulated using expressive Description Logics, for time sensitive applications necessitates the development of fast (near realtime) reasoning tools. Multicore processors are nowadays widespread across desktop, laptop, server, and even smartphone and tablets devices. The rise of such powerful execution environments calls for new parallel and distributed Description Logics (DLs) reasoning algorithms. Many sophisticated optimizations have been explored and have considerably enhanced DL reasoning with light ontologies. Non-determinism remains a main source of complexity for implemented systems handling ontologies relying on more expressive logics.In this work, we explore handling non-determinism with DL languages enabling qualified cardinality restrictions. We implement a fork/join parallel framework into our tableau-based algebraic reasoner, which handles qualified cardinality restrictions and nominals using in-equation solving. The preliminary results are encouraging and show that using a parallel framework with algebraic reasoning is worth investigating and more promising than parallelizing standard tableau-based reasoning.
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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 | 0.001 | 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 | 0.001 | 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