2021
Journal article  Open Access

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

Cardillo F. A., Straccia U.

FOS: Computer and information sciences  Concept inclusion axioms  Artificial Intelligence (cs.AI)  Real-valued AdaBoost  Artificial Intelligence  Boosting  OWL Ontology  Logic  Machine Learning  Fuzzy Logic  Computer Science - Artificial Intelligence  OWL 2 ontologies 

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.

Source: Fuzzy sets and systems 438 (2021): 164–186. doi:10.1016/j.fss.2021.07.002

Publisher: North-Holland, Amsterdam , Paesi Bassi


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BibTeX entry
@article{oai:it.cnr:prodotti:458770,
	title = {Fuzzy OWL-Boost: learning fuzzy concept inclusions via real-valued boosting},
	author = {Cardillo F. A. and Straccia U.},
	publisher = {North-Holland, Amsterdam , Paesi Bassi},
	doi = {10.1016/j.fss.2021.07.002 and 10.48550/arxiv.2008.05297},
	journal = {Fuzzy sets and systems},
	volume = {438},
	pages = {164–186},
	year = {2021}
}

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