Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the increasing number of applications of Description Logics (DLs), unsatisfiable concepts and inconsistent knowledge bases become quite common, especially when the knowledge bases are large and complex. This makes it challenging, even for experienced knowledge engineers, to identify and resolve these unsatisfiabilities and inconsistencies manually. It is thus crucial to provide services to explain how and why a result is derived. Motivated by the possibility of applying resolution technique in first‐order logic to construct explanations for DLs, we present an algorithm that uses patterns to generate explanations for unsatisfiability and inconsistency reasoning in , obtained by extending our previous work on . The use of resolution proofs to provide explanations for DL reasoners is due to their focus which, through literals involved in the process, contributes directly to the contradiction, hence acting as filters to discard irrelevant information. We also establish the soundness and completeness of the algorithm. The proposed solution approach is independent of the underlying DL reasoners, which suggests its potential application for any DL framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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