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.
Fuzzy OWL-Boost: learning fuzzy concept inclusions via real-valued boosting
Cardillo FA;Straccia U
2021
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 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.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.ancejournal | FUZZY SETS AND SYSTEMS | en |
| dc.authority.orgunit | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | en |
| dc.authority.orgunit | Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI | en |
| dc.authority.people | Cardillo FA | en |
| dc.authority.people | Straccia U | en |
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| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
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| dc.date.accessioned | 2024/02/20 20:38:38 | - |
| dc.date.available | 2024/02/20 20:38:38 | - |
| dc.date.issued | 2021 | - |
| dc.description.abstracteng | 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. | - |
| dc.description.affiliations | CNR-ILC, Pisa, Italy; CNR-ISTI, Pisa, Italy | - |
| dc.description.allpeople | Cardillo, Fa; Straccia, U | - |
| dc.description.allpeopleoriginal | Cardillo F.A.; Straccia U. | en |
| dc.description.fulltext | partially_open | en |
| dc.description.note | Version of Record: Fuzzy Sets and Systems, vol. 438 (2022) pp. 164-186 | en |
| dc.description.numberofauthors | 2 | - |
| dc.identifier.doi | 10.1016/j.fss.2021.07.002 | en |
| dc.identifier.isi | WOS:000796486300009 | en |
| dc.identifier.scopus | 2-s2.0-85111013597 | en |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/402940 | - |
| dc.identifier.url | https://www.sciencedirect.com/science/article/abs/pii/S0165011421002426 | en |
| dc.language.iso | eng | en |
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| dc.relation.lastpage | 186 | en |
| dc.relation.medium | ELETTRONICO | en |
| dc.relation.numberofpages | 23 | en |
| dc.relation.volume | 438 | en |
| dc.subject.keywordseng | OWL Ontology | - |
| dc.subject.keywordseng | Machine Learning | - |
| dc.subject.keywordseng | Fuzzy Logic | - |
| dc.subject.keywordseng | Boosting | - |
| dc.subject.singlekeyword | OWL Ontology | * |
| dc.subject.singlekeyword | Machine Learning | * |
| dc.subject.singlekeyword | Fuzzy Logic | * |
| dc.subject.singlekeyword | Boosting | * |
| dc.title | Fuzzy OWL-Boost: learning fuzzy concept inclusions via real-valued boosting | en |
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| isi.authority.ancejournal | FUZZY SETS AND SYSTEMS###0165-0114 | * |
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| isi.contributor.affiliation | Consiglio Nazionale delle Ricerche (CNR) | - |
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| isi.contributor.country | Italy | - |
| isi.contributor.country | Italy | - |
| isi.contributor.name | Franco Alberto | - |
| isi.contributor.name | Umberto | - |
| isi.contributor.researcherId | CJZ-8798-2022 | - |
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| isi.contributor.subaffiliation | Ist Sci & Tecnol Informaz | - |
| isi.contributor.surname | Cardillo | - |
| isi.contributor.surname | Straccia | - |
| isi.date.issued | 2022 | * |
| isi.description.abstracteng | 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. (c) 2021 Elsevier B.V. All rights reserved. | * |
| isi.description.allpeopleoriginal | Cardillo, FA; Straccia, U; | * |
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| isi.publisher.place | RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS | * |
| isi.relation.firstpage | 164 | * |
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| isi.title | Fuzzy OWL-Boost: Learning fuzzy concept inclusions via real-valued boosting | * |
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| scopus.contributor.name | Umberto | - |
| scopus.contributor.subaffiliation | Istituto di Linguistica Computazionale; | - |
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| scopus.contributor.surname | Cardillo | - |
| scopus.contributor.surname | Straccia | - |
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| scopus.description.abstracteng | 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. | * |
| scopus.description.allpeopleoriginal | Cardillo F.A.; Straccia U. | * |
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| scopus.funding.funders | 100010661 - Horizon 2020 Framework Programme; 100010661 - Horizon 2020 Framework Programme; 501100007601 - Horizon 2020; | * |
| scopus.funding.ids | 952215; | * |
| scopus.identifier.doi | 10.1016/j.fss.2021.07.002 | * |
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| scopus.subject.keywords | Concept inclusion axioms; Fuzzy logic; Machine learning; OWL 2 ontologies; Real-valued AdaBoost; | * |
| scopus.title | Fuzzy OWL-Boost: Learning fuzzy concept inclusions via real-valued boosting | * |
| scopus.titleeng | Fuzzy OWL-Boost: Learning fuzzy concept inclusions via real-valued boosting | * |
| Appare nelle tipologie: | 01.01 Articolo in rivista | |
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