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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Athabasca University Press
2017-02-01
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| Series: | International Review of Research in Open and Distributed Learning |
| Subjects: | |
| Online Access: | http://www.irrodl.org/index.php/irrodl/article/view/2681 |
| _version_ | 1828959147337449472 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T09:02:23Z |
| format | Article |
| id | doaj.art-597325821b29426c86d66a6f28e7af30 |
| institution | Directory Open Access Journal |
| issn | 1492-3831 |
| language | English |
| last_indexed | 2024-12-14T09:02:23Z |
| publishDate | 2017-02-01 |
| publisher | Athabasca University Press |
| record_format | Article |
| series | International Review of Research in Open and Distributed Learning |
| spelling | doaj.art-597325821b29426c86d66a6f28e7af302022-12-21T23:08:48ZengAthabasca University PressInternational Review of Research in Open and Distributed Learning1492-38312017-02-0118110.19173/irrodl.v18i1.2681Instance-Based Ontology Matching For Open and Distance Learning MaterialsSergio Cerón-Figueroa0Itzamá López-Yáñez1Yenny Villuendas-Rey2Oscar Camacho-Nieto3Mario Aldape-Pérez4Cornelio Yáñez-Márquez5Instituto Politécnico Nacional, MéxicoInstituto Politécnico Nacional, MéxicoInstituto Politécnico Nacional, MéxicoInstituto Politécnico Nacional, MéxicoInstituto Politécnico Nacional, MéxicoInstituto Politécnico Nacional, MéxicoThe 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.http://www.irrodl.org/index.php/irrodl/article/view/2681open and distance learningontology matching probleme-learningpattern recognitionassociative classifier |
| spellingShingle | 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 International Review of Research in Open and Distributed Learning open and distance learning ontology matching problem e-learning pattern recognition associative classifier |
| title | 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_short | Instance-Based Ontology Matching For Open and Distance Learning Materials |
| title_sort | instance based ontology matching for open and distance learning materials |
| topic | open and distance learning ontology matching problem e-learning pattern recognition associative classifier |
| url | http://www.irrodl.org/index.php/irrodl/article/view/2681 |
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