Absorption for ABoxes with Local Universal Restrictions.
Bibliographic record
Abstract
Abstract. We elaborate on earlier work in which we developed a novel method for evaluating instance queries over DL knowledge bases that derives from binary absorption. An important feature of this earlier method and its refinement in this paper is that they avoid the need to check explicitly for consistency, a property that is desirable, for example, in SPARQL query evaluation over RDF data sets that can dynamically include sophisticated ontologies. In particular, we resolve a number of outstanding issues with the earlier method that limited its capabilities for knowledge bases that involve an extensive use of typing constraints expressed as axioms of the form A ⊑ ∀R.B, or that require and use both role hierarchies and transitive roles. We also show how our more general method supports a safe use of nominals in instance queries, and how the method can therefore be used to evaluate basic graph patterns in the SPARQL query language. Finally, we present the results of a preliminary experimental evaluation that validates the efficacy of our more refined method for instance checking. 1
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".