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

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jun 2022Embargo end date: 01 Jan 2020 Italy English Publisher:Elsevier BVJournal:Fuzzy Sets and Systems, volume 438, pages 164-186 (issn: 0165-0114,

Copyright policy )Funded by:EC | TAILOR, EC | DeepHealth

Authors: Cardillo, Franco Alberto; Straccia, Umberto;

doi: 10.1016/j.fss.2021.07.002 , 10.48550/arxiv.2008.05297

arXiv: 2008.05297

handle: 20.500.14243/402940

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

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Abstract

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 a target class T of an OWL ontology, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T. 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. An interesting feature is that the learned rules can be represented directly into Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T.

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Italy

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Keywords

FOS: Computer and information sciences, real-valued AdaBoost, Computer Science - Artificial Intelligence, Learning and adaptive systems in artificial intelligence, Concept inclusion axioms, Reasoning under uncertainty in the context of artificial intelligence, Boosting, Fuzzy logic, OWL Ontology, Machine Learning, machine learning, Artificial Intelligence (cs.AI), Fuzzy Logic, Knowledge representation, Machine learning, fuzzy logic, concept inclusion axioms, Real-valued AdaBoost, OWL 2 ontologies

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