arXiv (Cornell University) · 2017 · 15 citations · 19 references
ComputingComputer ScienceAlgorithmsKnowledge RepresentationLogicDescription LogicsFormal OntologyOntology DesignOntology EngineeringQuestion AnsweringComplexityDescription Logic OntologiesLightweight Description LogicsLightweight OntologiesOntologiesPolynomial Time ComplexityPolynomial-time AlgorithmsWeb Ontology Language
We study the problem of learning description logic (DL) ontologies in Angluin et al.'s framework of exact learning via queries. We admit membership queries ("is a given subsumption entailed by the target ontology?") and equivalence queries ("is a given ontology equivalent to the target ontology?"). We present three main results: (1) ontologies formulated in (two relevant versions of) the description logic DL-Lite can be learned with polynomially many queries of polynomial size; (2) this is not the case for ontologies formulated in the description logic EL, even when only acyclic ontologies are admitted; and (3) ontologies formulated in a fragment of EL related to the web ontology language OWL 2 RL can be learned in polynomial time. We also show that neither membership nor equivalence queries alone are sufficient in cases (1) and (3).
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Learning regular sets from queries and counterexamples
Dana Angluin · Information and Computation · 1987 · 2.1K citations
Dana Angluin · Machine Learning · 1988 · 1.6K citations · Full text
Franz Baader, Sebastian Brandt, Carsten Lutz · 2005 · 869 citations · Full text