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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/135233
Ver los metadatos del registro completo
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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 Articulo 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://sedici.unlp.edu.ar/handle/10915/135233 |
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http://sedici.unlp.edu.ar/handle/10915/135233 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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