Using semantic enrichment methods in expert search system for recruitment process in IT corporation

Abstract

The problem of intelligent information retrieval and semantic enrichment becomes more and more popular due to the difficulty of searching and analyzing large text datasets. The common approach assumes user manual queries in natural language. Various semantic enrichment methods and intelligent text searching allow obtaining more accurate results leading to broader knowledge and user satisfaction.This research presents state-of-the-art methods of searching with enrichment and building rankings of results for the expert recruitment process in IT industry. The proposed model implements full-text search, semantic enrichment, and machine learning to match experts with job offers. Different data sources on expert competencies were used, including curricula vitae, historical data, and Internet resources. The testing results confirm an improvement in the search quality compared to the existing systems in the recruitment company.