Title

Technical Papers Session V: An ontology-based knowledge management model for e-recruitment utilizing MOOCs data

Abstract/Description

In the recent past, the role and importance of Knowledge Management have become very visible in an organizational learning environment. This paper aims to cover the implementation of knowledge management in the process of development of e-recruitment platform using an ontology-based approach. The approach has been modeled in such a way that it can facilitate employers for hiring and recruiting purposes as per the organization's desired requirements with an effective utilization of MOOCs data. For the end-users, the developed ontology will be able to recommend candidates to the e-recruiter based on the required functions of the job post, and similarly students or eligible candidates who have completed MOOC will be able to view job vacancies based on their field of interest. The proposed model also incorporates the data of students, including the required information about the enrollment, grades, and skills attained in various MOOCs.

Location

Lecture Hall B (Aman Tower, 11th floor)

Session Theme

Technical Papers Session V - Social Computing & Information Retrieval

Session Type

Parallel Technical Session

Session Chair

Dr. Tariq Rahim Soomro

Start Date

17-11-2019 2:20 PM

End Date

17-11-2019 2:40 PM

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Nov 17th, 2:20 PM Nov 17th, 2:40 PM

Technical Papers Session V: An ontology-based knowledge management model for e-recruitment utilizing MOOCs data

Lecture Hall B (Aman Tower, 11th floor)

In the recent past, the role and importance of Knowledge Management have become very visible in an organizational learning environment. This paper aims to cover the implementation of knowledge management in the process of development of e-recruitment platform using an ontology-based approach. The approach has been modeled in such a way that it can facilitate employers for hiring and recruiting purposes as per the organization's desired requirements with an effective utilization of MOOCs data. For the end-users, the developed ontology will be able to recommend candidates to the e-recruiter based on the required functions of the job post, and similarly students or eligible candidates who have completed MOOC will be able to view job vacancies based on their field of interest. The proposed model also incorporates the data of students, including the required information about the enrollment, grades, and skills attained in various MOOCs.