Research map / Learning theory and algorithms / Combinatorial and search optimization
Repairing Ontologies via Axiom Weakening
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace removal with weakening, selecting bad axioms by sampling minimally inconsistent sets | Ontology repair · Ten BioPortal ontologies | qual: 0.72 (0.36); 0.76 (0.31); 0.72 (0.27); 0.71 (0.31); 0.71 (0.40); 0.76 (0.29); 0.70 (0.35); 0.67 (0.41); 0.82 (0.36); 0.6 | Helped |
| Replace removal with weakening, selecting bad axioms randomly | Ontology repair · co-wheat, elig, hom, ofsmr, xeo | qual: 0.69 (0.29); 0.61 (0.30); 0.68 (0.26); 0.65 (0.31); 0.67 (0.29) | Helped |
| Replace removal with weakening, selecting bad axioms randomly | Ontology repair · bctt, icd11, ogr, pe, taxrank | qual: 0.55 (0.35); 0.60 (0.30); 0.56 (0.32); 0.56 (0.33); 0.56 (0.31) | No clear effect |
| Select the bad axioms through minimally inconsistent sets rather than randomly | RepairOntologyWeaken vs RepairOntologyRemove · Ten BioPortal ontologies | qual: MIS results range from 0.67 (0.34) to 0.82 (0.36); random results range from 0.55 (0.35) to 0.69 (0.29) | Helped |
Closest papers by meaning
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