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

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 kno...

Descripción completa

Detalles Bibliográficos
Autores: Rubiolo, Mariano, Caliusco, Maria Laura, Stegmayer, Georgina, Coronel, M., Gareli Fabrizi, M.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2012
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/196290
Acceso en línea:http://hdl.handle.net/11336/196290
Access Level:acceso abierto
Palabra clave:ARTIFICIAL NEURAL NETWORK
KNOWLEDGE-SOURCE DISCOVERY
SEMANTIC WEB
WORDNET
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
id AR_ec344c6b250a0bcf3de1010a8b4e2c1b
oai_identifier_str oai:ri.conicet.gov.ar:11336/196290
network_acronym_str AR
network_name_str Argentina
repository_id_str
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 Técnicas2024-05-08T13:58:20Zoai: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:34982024-05-08 13:58:20.961CONICET 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
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
topic ARTIFICIAL NEURAL NETWORK
KNOWLEDGE-SOURCE DISCOVERY
SEMANTIC WEB
WORDNET
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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
_version_ 1799195623599112192
score 15,228081

Similares en LA Referencia