Ontology based multi criteria recomanded system to guide internship assignment process

Abstract : Internship component is a vital part of the university training program as it looked as a vital resource for students to gain the required skills for employment. Recognizing the importance of drawing a compromise between companies' requirements and students' skills in the internship assignment process has trigged the need of a multi criteria decision system that enables to select the right student to the right internship. Both competencies required by the recruiters and the students' skills evolve in the time and their regular update is necessary. This leads to the development of a reference model for their management, update and maintenance. This paper proposes a multi criteria decision system which aims at integrating an ontology in order to select and to recommend adapted internship seekers to the recruiter mission posting or vice versa. This proposed recommended system has four phases in screening candidates for recruitment. In the first phase, mission requirements are represented as ontology. The system collects candidates' resumes and constructs ontology model for the features of the students in the second phase. In the third phase, we construct the training courses ontology. Finally, we discuss the steps of the ontology based multi criteria decision making method that enables to retrieve the right candidates to the right internship.
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Communication dans un congrès
CSCI 2016 - International Conference on Computational Science and Computational Intelligence, Dec 2016, Las Vegas, United States
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https://hal.archives-ouvertes.fr/hal-01788507
Contributeur : Yacine Ouzrout <>
Soumis le : mardi 6 novembre 2018 - 16:08:21
Dernière modification le : lundi 19 novembre 2018 - 16:01:45

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  • HAL Id : hal-01788507, version 1

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Abir M'Baya, Jannik Laval, Néjib Moalla, Yacine Ouzrout, Abdelaziz Bouras. Ontology based multi criteria recomanded system to guide internship assignment process. CSCI 2016 - International Conference on Computational Science and Computational Intelligence, Dec 2016, Las Vegas, United States. 〈hal-01788507〉

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