Nejib Moalla

2papers

2 Papers

AIJan 18, 2017
Ontology based system to guide internship assignment process

Abir M 'Baya, Jannik Laval, Nejib Moalla et al.

Internship assignment is a complicated process for universities since it is necessary to take into account a multiplicity of variables to establish a compromise between companies' requirements and student competencies acquired during the university training. These variables build up a complex relations map that requires the formulation of an exhaustive and rigorous conceptual scheme. In this research a domain ontological model is presented as support to the student's decision making for opportunities of University studies level of the University Lumiere Lyon 2 (ULL) education system. The ontology is designed and created using methodological approach offering the possibility of improving the progressive creation, capture and knowledge articulation. In this paper, we draw a balance taking the demands of the companies across the capabilities of the students. This will be done through the establishment of an ontological model of an educational learners' profile and the internship postings which are written in a free text and using uncontrolled vocabulary. Furthermore, we outline the process of semantic matching which improves the quality of query results.

SEMay 15, 2015
A multi-criteria service selection algorithm for business process requirements

Sophea Chhun, Chantal Cherifi, Nejib Moalla et al.

The selection of the most appropriate Web services to realize business tasks still remain an open issue. We propose a multi-criteria algorithm for efficient service selection. Web services and their QoS values are stored in a Web service ontology (WSOnto) and business processes are modeled with the BPMN2.0 specifications. Our algorithm performs an instance-based ontology matching between the WSOnto and the business process ontology. The business context, functional properties and QoS values of Web services are considered. The algorithm computes the variation of QoS values over times. This strategy allows better accurate Web services ranking relevant to a user's request.