Evidential reasoning for the treatment of incoherent terminologies
Bibliographic record
Abstract
Many reasoning algorithms and techniques require consistent terminologies to be able to operate correctly and efficiently. However, many ontologies become inconsistent during their evolution and lifecycle. Many methods have been proposed to handle inconsistent terminologies including those that tolerate or repair inconsistencies. Most of these approaches focus on the syntactic properties of ontology terminologies and attempt to address inconsistency from that perspective and satisfy postulates such as the principle of minimal change. In this paper, we will employ evidential reasoning to take into account assertional statements of an ontology as observations and probable indications for the correctness and validity of one axiom over other competing axioms. We will show how ontology assertions are beneficial in ranking axioms to be used in Reiter's hitting set algorithm.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| 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".