Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes

Mihaela-Irina Enachescu ()

Informatica Economica, 2019, vol. 23, issue 4, 51-65

Abstract: While trying to fill in empty positions in a short time frame, struggling to find the best candidates while competing with other recruiters for them, nowadays, HR personnel need to consider innovative ways for reaching faster the IT professionals. Manually searching across professional networks is no longer an option. This study introduces the prototype of a system that automatically screens the candidates in the IT field. Its main goal is to provide a valuable support in the first stage of the personnel selection by decreasing the number of errors that can occur when thousands of CVs/profiles are manually filtered to pick candidates for an interview. Our proposed system consists in a mobile application that automatically selects online profiles from professional websites (like Indeed, LinkedIn, Monster) and ranks them, to finally display the eligible candidates for a particular open position to the recruiter. We developed an ontology to support the matching between the knowledge in the candidate’s resume and the requirements in the job description. While developing the ontology our primary focus was on the skills that are encompassed in a resume, as these are the key abilities when searching for the ideal candidate. The knowledge a job seeker should possess, respectively a job description requires, is divided in the following categories: programming languages, databases, frameworks, integrated development environments, methodologies and operating systems. First part of the implementation, automatically extracting the skills from unstructured resumes, was achieved using Apache Tika and GATE.

Keywords: eRecruitment; Human Resource Ontology; Resume Screening; Semantic Web (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:23:y:2019:i:4:p:51-65

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