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