Learning Terminological Bayesian Classifiers - A Comparison of Alternative Approaches to Dealing with Unknown Concept-Memberships.
Pasquale Minervini, Claudia d’Amato, Nicola Fanizzi · 2012
Abstract. Knowledge available through Semantic Web representation formalisms can be missing, i.e. it is not always possible to infer the truth value of an assertion (due to the Open World Assumption). We propose a method for incrementally inducing terminological (tree-augmented) naïve Bayesian classifiers, which aim at estimating the probability that an individual belongs to a target concept given its membership to a learned set of Description Logic concepts. We then evaluate the impact of employing different methods of handling assertions whose truth value is unknown, each consistent with a different assumption on the ignorance model. 1