AN ONTOLOGY BASED APPROACH FOR IMPROVING JOB SEARCH IN ONLINE JOB PORTALS

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dc.contributor.authorDADA, OLUWATOBI SILAS
dc.date.accessioned2018-09-17T13:58:59Z
dc.date.available2018-09-17T13:58:59Z
dc.date.issued2017-10
dc.descriptionA THESIS SUBMITTED TO THE SCHOOL OF POST GRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF A MASTER OF SCIENCE DEGREE IN COMPUTER SCIENCE DEPARTMENT OF COMPUTER SCIENCE, FACULTY OF PHYSICAL SCIENCE, AHMADU BELLO UNIVERSITY, ZARIA, NIGERIAen_US
dc.description.abstractInternet has become the primary medium for Human Resource Management, specifically job recruitment and employment process. Most classical job recruitment portals on the internet rely solely on the keyword based search technique in plain text to locate jobs. However, this technique results in high recall with low precision and also without considering the semantic similarity between these keywords. Many researchers have also proposed several semantic matching approaches by developing ontologies as a reference to determine matching accuracy qualitatively, however these approaches do not quantify how closely matched applicants and employers are based on core skills. This dissertation proposes a technique that uses an ontology based approach to enhance keyword searching by leveraging on the similarity between concepts in the ontology, which represent core skills needed and required for a job in order to determine how closely matched an applicant is to a job advertisement and vice-versa . This was achieved by developing a Curriculum Vitae (CV) Ontology, annotating applicant profile and job postings using a common vocabulary and modifying the semantic concept similarity algorithm to accurately compute and rank matching score between profiles when a query is performed. The model was compared with the work of Tran (2016). The results showed that improvements were achieved in overall matching accuracy between core skills supplied by applicants and those required by employers. Improvements of 54% and 36% were obtained for Recall and F-measure respectively, over Tran (2016).en_US
dc.identifier.urihttp://hdl.handle.net/123456789/10454
dc.language.isoenen_US
dc.subjectONTOLOGY,en_US
dc.subjectAPPROACH,en_US
dc.subjectIMPROVING JOB SEARCH,en_US
dc.subjectONLINE JOB PORTALSen_US
dc.titleAN ONTOLOGY BASED APPROACH FOR IMPROVING JOB SEARCH IN ONLINE JOB PORTALSen_US
dc.typeThesisen_US
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