Instance-Based Ontology Matching For Open and Distance Learning Materials

The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Onto...

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Autores principales:	Sergio Cerón-Figueroa, Itzamá López-Yáñez, Yenny Villuendas-Rey, Oscar Camacho-Nieto, Mario Aldape-Pérez, Cornelio Yáñez-Márquez
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2017
Materias:	open and distance learning ontology matching problem e-learning pattern recognition associative classifier Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/597325821b29426c86d66a6f28e7af30
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spelling	oai:doaj.org-article:597325821b29426c86d66a6f28e7af302021-12-02T17:16:18ZInstance-Based Ontology Matching For Open and Distance Learning Materials10.19173/irrodl.v18i1.26811492-3831https://doaj.org/article/597325821b29426c86d66a6f28e7af302017-02-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/2681https://doaj.org/toc/1492-3831The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies —both in the Learning Objects Metadata (LOM) format— into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art. Sergio Cerón-FigueroaItzamá López-YáñezYenny Villuendas-ReyOscar Camacho-NietoMario Aldape-PérezCornelio Yáñez-MárquezAthabasca University Pressarticleopen and distance learningontology matching probleme-learningpattern recognitionassociative classifierSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 18, Iss 1 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	open and distance learning ontology matching problem e-learning pattern recognition associative classifier Special aspects of education LC8-6691
spellingShingle	open and distance learning ontology matching problem e-learning pattern recognition associative classifier Special aspects of education LC8-6691 Sergio Cerón-Figueroa Itzamá López-Yáñez Yenny Villuendas-Rey Oscar Camacho-Nieto Mario Aldape-Pérez Cornelio Yáñez-Márquez Instance-Based Ontology Matching For Open and Distance Learning Materials
description	The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies —both in the Learning Objects Metadata (LOM) format— into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art.
format	article
author	Sergio Cerón-Figueroa Itzamá López-Yáñez Yenny Villuendas-Rey Oscar Camacho-Nieto Mario Aldape-Pérez Cornelio Yáñez-Márquez
author_facet	Sergio Cerón-Figueroa Itzamá López-Yáñez Yenny Villuendas-Rey Oscar Camacho-Nieto Mario Aldape-Pérez Cornelio Yáñez-Márquez
author_sort	Sergio Cerón-Figueroa
title	Instance-Based Ontology Matching For Open and Distance Learning Materials
title_short	Instance-Based Ontology Matching For Open and Distance Learning Materials
title_full	Instance-Based Ontology Matching For Open and Distance Learning Materials
title_fullStr	Instance-Based Ontology Matching For Open and Distance Learning Materials
title_full_unstemmed	Instance-Based Ontology Matching For Open and Distance Learning Materials
title_sort	instance-based ontology matching for open and distance learning materials
publisher	Athabasca University Press
publishDate	2017
url	https://doaj.org/article/597325821b29426c86d66a6f28e7af30
work_keys_str_mv	AT sergioceronfigueroa instancebasedontologymatchingforopenanddistancelearningmaterials AT itzamalopezyanez instancebasedontologymatchingforopenanddistancelearningmaterials AT yennyvilluendasrey instancebasedontologymatchingforopenanddistancelearningmaterials AT oscarcamachonieto instancebasedontologymatchingforopenanddistancelearningmaterials AT marioaldapeperez instancebasedontologymatchingforopenanddistancelearningmaterials AT cornelioyanezmarquez instancebasedontologymatchingforopenanddistancelearningmaterials
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Instance-Based Ontology Matching For Open and Distance Learning Materials

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