A fuzzy logic and ontology-based approach for improving the CV and job offer matching in recruitment process. (6th January 2022)

• Record Type: Journal Article
• Authors: Habous, Amine; Nfaoui, El Habib
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 15:Number 2(2021)
• Page Start: 104
• Page End: 120
• Publication Date: 2022-01-06
• Subjects: text mining -- natural language processing -- feature extraction -- metadata weighting -- ICT recruitment -- fuzzy logic -- machine learning; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

Abstract

The recruitment process is a critical activity for every organisation, and it allows to find the appropriate candidate for a job offer and its employer work criteria. The competitive nature of the recruitment environment makes the task of hiring new employees very hard for companies due to the high number of CV (resume) and profiles to process, the personal job interests, the customised requirements and precise skills requested by employees, etc. The time becomes crucial for recruiters' choices; consequently, it might impact the selection process quality. In this paper, we propose a retrieval system for automating the matching process between the candidate CV and the job offer. It is designed based on Natural Language Processing, machine learning and fuzzy logic to handle the matching between the job description and the CV. It also considers the proficiency level for the technology skills. Moreover, it offers an estimation of the overall CV/job offer expertise level. In that way, it overcomes the under-qualification and over-qualification issues in the ICT (Information and Communication Technologies) recruitment process. Experimental results on a ground-truth data of a recruiter company demonstrate that our proposal provides effective results.