Improvement of emploitic's search engine results using an ontological approach

Abstract

Launched in 2006, Emploitic.com is the leading recruitment website in Algeria. Its goal is to facil-itate job searches for candidates and allow recruiters to ?nd the best pro?les with ease. Emploitic allows users to publish their professional information (training, work history, career summary, so-cial links, etc.) to be used by recruiters when identifying new candidates or to obtain additional information about them. The company achieved its vision by means of a probabilistic information retrieval model developed using the Xapian toolkit. However, as the number of users in the website grew the limits of the old probabilistic model have been reached due to the emergence of a large number of users and their accompanied job posts and resumes. A poor relevance of results retrieved has become noticeable when handling such a sizeable dataset on account of the imitations opposed by the probabilistic model, which are no longer acceptable, which raises the issue to look for new solutions. One prime candidate solution is extending the system with a new service through the use of existing ontology for human resources generated by Emploitic company. Furthermore, the addition of an evaluator software system permitted an assessment of the performance for both old and improved versions of the search engine, showing a clear improvement in the relevance of retrieved documents, in addition, validating the newly developed system as an acceptable solution to this binary classi?cation problem.