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

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 n...

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Bibliographic Details
Main Author: Mihaela-Irina ENACHESCU
Format: Article
Language:English
Published: Inforec Association 2019-01-01
Series:Informatică economică
Subjects:
Online Access:http://revistaie.ase.ro/content/92/05%20-%20enachescu.pdf
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spelling doaj-49212a0fef404990a5affb639e10d1902020-11-25T02:39:15ZengInforec AssociationInformatică economică1453-13051842-80882019-01-01234516510.12948/issn14531305/23.4.2019.05Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured ResumesMihaela-Irina ENACHESCUWhile 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.http://revistaie.ase.ro/content/92/05%20-%20enachescu.pdferecruitmenthuman resource ontologyresume screeningsemantic web
collection DOAJ
language English
format Article
sources DOAJ
author Mihaela-Irina ENACHESCU
spellingShingle Mihaela-Irina ENACHESCU
Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes
Informatică economică
erecruitment
human resource ontology
resume screening
semantic web
author_facet Mihaela-Irina ENACHESCU
author_sort Mihaela-Irina ENACHESCU
title Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes
title_short Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes
title_full Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes
title_fullStr Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes
title_full_unstemmed Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes
title_sort screening the candidates in it field based on semantic web technologies: automatic extraction of technical competencies from unstructured resumes
publisher Inforec Association
series Informatică economică
issn 1453-1305
1842-8088
publishDate 2019-01-01
description 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.
topic erecruitment
human resource ontology
resume screening
semantic web
url http://revistaie.ase.ro/content/92/05%20-%20enachescu.pdf
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