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Candidate axiom scoring is the task of assessing the acceptability of acandidate axiom against the evidence provided by known facts or data. Theability to score candidate axioms reliably is required for automated schema orontology induction, but it can also be valuable for ontology and/or knowledgegraph validation. Accurate axiom scoring heuristics are often computationallyexpensive, which is an issue if you wish to use them in iterative searchtechniques like level-wise generate-and-test or evolutionary algorithms, whichrequire scoring a large number of candidate axioms. We address the problem ofdeveloping a predictive model as a substitute for reasoning that predicts thepossibility score of candidate class axioms and is quick enough to be employedin such situations. We use a semantic similarity measure taken from anontology's subsumption structure for this purpose. We show that the approachprovided in this work can accurately learn the possibility scores of candidateOWL class axioms and that it can do so for a variety of OWL class axioms.

Predicting the Score of Atomic Candidate OWL Class Axioms