Research Article Open Access

OntoJob Query Processor: An Ontology Driven Query Processing Method in Jobology Information System

Ranjna Jain1 and Neelam Duhan1
  • 1 J.C. Bose University of Science and Technology, India
Journal of Computer Science
Volume 16 No. 5, 2020, 702-714

DOI: https://doi.org/10.3844/jcssp.2020.702.714

Submitted On: 10 May 2019 Published On: 11 January 2021

How to Cite: Jain, R. & Duhan, N. (2020). OntoJob Query Processor: An Ontology Driven Query Processing Method in Jobology Information System. Journal of Computer Science, 16(5), 702-714. https://doi.org/10.3844/jcssp.2020.702.714

Abstract

There are a large numbers of jobsites/job portals that provide information about employment on the internet. These websites facilitate employers to post job lists. Job seekers go through those job posts and apply for the same. But, due to the availability of dozens of job portals, job seekers are unable to concentrate on the efforts to see the best outcomes. The overall objective of the paper is to develop a prototype system that provides a platform for the job seeker to access all the job lists from various job sites on a single click, at the same place with respect to the fired query. For this purpose, an ontology driven information system named as Jobology is discussed that integrates various Jobboards using the approach of ontology alignment. The system takes user’s query in keyword format and in response generates a set of SPARQL queries. These SPARQL queries are then fired on their respective ontologies and in turn they yield the results. These results are merged and finally presented to user. As a contribution of this paper, we have proposed an “OntoJob” Query processor that takes job seeker query in keyword format and in turn generates a set of SPARQL queries with respect to every jobboard. The proposed approach is implemented in JAVA using OWLAPI on window platform. To evaluate the proposed work, comparison analysis between Jobboards, proposed ontologies and integrated system was performed. The results came out to be very satisfactory.

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Keywords

  • Ontology
  • Semantic Web
  • Data Heterogeneity
  • Query Processing
  • SPARQL