Fuzzy OWL-Boost: Learning fuzzy concept inclusions via real-valued boosting

OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given an OWL ontology and a target class T , we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T (and to which degree). To do so, we present FUZZY OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation with several ontologies. The attached PDF corresponds to the preprint version at: https://doi.org/10.48550/arXiv.2008.0529