Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

Autores
Rubiolo, Mariano; Caliusco, Maria Laura; Stegmayer, Georgina; Coronel, M.; Gareli Fabrizi, M.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources, which are relevant to a user´s request. New eficient approaches for developing web intelligence and helping users to avoid irrelevant search results on the web have recently appeared. Artificial Neural Networks (ANN) being one of the most recent ones. However,there still remains a lot of work to be done in this area. This work makes a contribution to the field of knowledge-resource discovery and ontology matching techniques for the Semantic Web by presenting an approach which is based on an ANN classifier. Experimental results show that the ANN-based ontology matching model has provided satisfactory responses to the test cases.
Fil: Rubiolo, Mariano. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Caliusco, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Coronel, M.. Universidad Tecnológica Nacional; Argentina
Fil: Gareli Fabrizi, M.. Universidad Tecnológica Nacional; Argentina
Materia
ARTIFICIAL NEURAL NETWORK
KNOWLEDGE-SOURCE DISCOVERY
SEMANTIC WEB
WORDNET
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/196290
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network modelRubiolo, MarianoCaliusco, Maria LauraStegmayer, GeorginaCoronel, M.Gareli Fabrizi, M.ARTIFICIAL NEURAL NETWORKKNOWLEDGE-SOURCE DISCOVERYSEMANTIC WEBWORDNEThttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources, which are relevant to a user´s request. New eficient approaches for developing web intelligence and helping users to avoid irrelevant search results on the web have recently appeared. Artificial Neural Networks (ANN) being one of the most recent ones. However,there still remains a lot of work to be done in this area. This work makes a contribution to the field of knowledge-resource discovery and ontology matching techniques for the Semantic Web by presenting an approach which is based on an ANN classifier. Experimental results show that the ANN-based ontology matching model has provided satisfactory responses to the test cases.Fil: Rubiolo, Mariano. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Caliusco, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Coronel, M.. Universidad Tecnológica Nacional; ArgentinaFil: Gareli Fabrizi, M.. Universidad Tecnológica Nacional; ArgentinaElsevier Science Inc.2012-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/196290Rubiolo, Mariano; Caliusco, Maria Laura; Stegmayer, Georgina; Coronel, M.; Gareli Fabrizi, M.; Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model; Elsevier Science Inc.; Information Sciences; 194; 7-2012; 107-1190020-0255CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2011.08.008info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:28Zoai:ri.conicet.gov.ar:11336/196290instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:58:28.854CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
title Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
spellingShingle Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
Rubiolo, Mariano
ARTIFICIAL NEURAL NETWORK
KNOWLEDGE-SOURCE DISCOVERY
SEMANTIC WEB
WORDNET
title_short Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
title_full Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
title_fullStr Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
title_full_unstemmed Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
title_sort Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
dc.creator.none.fl_str_mv Rubiolo, Mariano
Caliusco, Maria Laura
Stegmayer, Georgina
Coronel, M.
Gareli Fabrizi, M.
author Rubiolo, Mariano
author_facet Rubiolo, Mariano
Caliusco, Maria Laura
Stegmayer, Georgina
Coronel, M.
Gareli Fabrizi, M.
author_role author
author2 Caliusco, Maria Laura
Stegmayer, Georgina
Coronel, M.
Gareli Fabrizi, M.
author2_role author
author
author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORK
KNOWLEDGE-SOURCE DISCOVERY
SEMANTIC WEB
WORDNET
topic ARTIFICIAL NEURAL NETWORK
KNOWLEDGE-SOURCE DISCOVERY
SEMANTIC WEB
WORDNET
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources, which are relevant to a user´s request. New eficient approaches for developing web intelligence and helping users to avoid irrelevant search results on the web have recently appeared. Artificial Neural Networks (ANN) being one of the most recent ones. However,there still remains a lot of work to be done in this area. This work makes a contribution to the field of knowledge-resource discovery and ontology matching techniques for the Semantic Web by presenting an approach which is based on an ANN classifier. Experimental results show that the ANN-based ontology matching model has provided satisfactory responses to the test cases.
Fil: Rubiolo, Mariano. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Caliusco, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Coronel, M.. Universidad Tecnológica Nacional; Argentina
Fil: Gareli Fabrizi, M.. Universidad Tecnológica Nacional; Argentina
description The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources, which are relevant to a user´s request. New eficient approaches for developing web intelligence and helping users to avoid irrelevant search results on the web have recently appeared. Artificial Neural Networks (ANN) being one of the most recent ones. However,there still remains a lot of work to be done in this area. This work makes a contribution to the field of knowledge-resource discovery and ontology matching techniques for the Semantic Web by presenting an approach which is based on an ANN classifier. Experimental results show that the ANN-based ontology matching model has provided satisfactory responses to the test cases.
publishDate 2012
dc.date.none.fl_str_mv 2012-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/196290
Rubiolo, Mariano; Caliusco, Maria Laura; Stegmayer, Georgina; Coronel, M.; Gareli Fabrizi, M.; Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model; Elsevier Science Inc.; Information Sciences; 194; 7-2012; 107-119
0020-0255
CONICET Digital
CONICET
url http://hdl.handle.net/11336/196290
identifier_str_mv Rubiolo, Mariano; Caliusco, Maria Laura; Stegmayer, Georgina; Coronel, M.; Gareli Fabrizi, M.; Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model; Elsevier Science Inc.; Information Sciences; 194; 7-2012; 107-119
0020-0255
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2011.08.008
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Inc.
publisher.none.fl_str_mv Elsevier Science Inc.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.885934

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