Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report

Autores
Ayub, María Celeste; Cian, Ayelén; Caliusco, María Laura; Reynares, Emiliano
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recently, Team Recommender Systems (TRS) have become ex-tremely common because they are software tools and techniques that helps to organizations to composite team needed to carry out a task requiring multiple skills. TRS have two important problems: (1) managing semantic heterogeneity that occurs when the data describing the same entities related to the real world is represented in different ways, and (2) specialization excess leading to display the objects of highest similarity with the user specified instead of a wide range of options leaving out of consideration the highest possible user interest infor-mation. In recent years, the ontology-based information systems have gained the attention of the researchers and practitioners since they handle the semantic heterogeneity problem. Despite of the advance done, building methodologies for developing ontology-based systems is still a research area. In this paper, we report our experience in developing an ontology-based TRS by using the EDON method. The developed TRS analyses human resource information to recom-mend a work team for a software development project.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
team recommender systems
ontology-based information system
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/135233

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spelling Developing an Ontology-Based Team Recommender System using EDON Method : An experience ReportAyub, María CelesteCian, AyelénCaliusco, María LauraReynares, EmilianoCiencias Informáticasteam recommender systemsontology-based information systemRecently, Team Recommender Systems (TRS) have become ex-tremely common because they are software tools and techniques that helps to organizations to composite team needed to carry out a task requiring multiple skills. TRS have two important problems: (1) managing semantic heterogeneity that occurs when the data describing the same entities related to the real world is represented in different ways, and (2) specialization excess leading to display the objects of highest similarity with the user specified instead of a wide range of options leaving out of consideration the highest possible user interest infor-mation. In recent years, the ontology-based information systems have gained the attention of the researchers and practitioners since they handle the semantic heterogeneity problem. Despite of the advance done, building methodologies for developing ontology-based systems is still a research area. In this paper, we report our experience in developing an ontology-based TRS by using the EDON method. The developed TRS analyses human resource information to recom-mend a work team for a software development project.Sociedad Argentina de Informática e Investigación Operativa2014-06-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf3-15http://sedici.unlp.edu.ar/handle/10915/135233enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/38info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/reference/hdl/10915/76373info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:34:01Zoai:sedici.unlp.edu.ar:10915/135233Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:34:01.604SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
title Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
spellingShingle Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
Ayub, María Celeste
Ciencias Informáticas
team recommender systems
ontology-based information system
title_short Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
title_full Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
title_fullStr Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
title_full_unstemmed Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
title_sort Developing an Ontology-Based Team Recommender System using EDON Method : An experience Report
dc.creator.none.fl_str_mv Ayub, María Celeste
Cian, Ayelén
Caliusco, María Laura
Reynares, Emiliano
author Ayub, María Celeste
author_facet Ayub, María Celeste
Cian, Ayelén
Caliusco, María Laura
Reynares, Emiliano
author_role author
author2 Cian, Ayelén
Caliusco, María Laura
Reynares, Emiliano
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
team recommender systems
ontology-based information system
topic Ciencias Informáticas
team recommender systems
ontology-based information system
dc.description.none.fl_txt_mv Recently, Team Recommender Systems (TRS) have become ex-tremely common because they are software tools and techniques that helps to organizations to composite team needed to carry out a task requiring multiple skills. TRS have two important problems: (1) managing semantic heterogeneity that occurs when the data describing the same entities related to the real world is represented in different ways, and (2) specialization excess leading to display the objects of highest similarity with the user specified instead of a wide range of options leaving out of consideration the highest possible user interest infor-mation. In recent years, the ontology-based information systems have gained the attention of the researchers and practitioners since they handle the semantic heterogeneity problem. Despite of the advance done, building methodologies for developing ontology-based systems is still a research area. In this paper, we report our experience in developing an ontology-based TRS by using the EDON method. The developed TRS analyses human resource information to recom-mend a work team for a software development project.
Sociedad Argentina de Informática e Investigación Operativa
description Recently, Team Recommender Systems (TRS) have become ex-tremely common because they are software tools and techniques that helps to organizations to composite team needed to carry out a task requiring multiple skills. TRS have two important problems: (1) managing semantic heterogeneity that occurs when the data describing the same entities related to the real world is represented in different ways, and (2) specialization excess leading to display the objects of highest similarity with the user specified instead of a wide range of options leaving out of consideration the highest possible user interest infor-mation. In recent years, the ontology-based information systems have gained the attention of the researchers and practitioners since they handle the semantic heterogeneity problem. Despite of the advance done, building methodologies for developing ontology-based systems is still a research area. In this paper, we report our experience in developing an ontology-based TRS by using the EDON method. The developed TRS analyses human resource information to recom-mend a work team for a software development project.
publishDate 2014
dc.date.none.fl_str_mv 2014-06-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/38
info:eu-repo/semantics/altIdentifier/issn/1514-6774
info:eu-repo/semantics/reference/hdl/10915/76373
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
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Creative Commons Attribution 4.0 International (CC BY 4.0)
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