ONTOLOGY BASED JOB-CANDIDATE MATCHING USING SKILL SETS

Abstract

Adopting an efficient skills matching approach is necessary for discovering right skills needed in the labour market. Hence, skills management has been recently acknowledged as one of the key factors to adequately face the increasing competitiveness among different companies. In fact, suitable knowledge representation and matching of skills and other competences in the job and individual profiles—if properly chosen—could support human resources management automation through suitable matching and ranking services. This thesis work presents an approach for matchmaking between skills demand and supply through the implementation of methodology for skill profiles enrichment and matching supply and demand profiles over multiple criteria. In this respect, this work brings together research from different fields – profile modelling, information enrichment and multi-criteria matching. Methodology for harmonization and enrichment of heterogeneous profile models and skill set description by making use of standard ontology is the first contribution of this work. Secondly the formulated solution utilizes algorithm for similarity matching across multi-criteria for discovering set of profiles that best fits the job description criteria. The system developed in the scope of this thesis work can provide a foundation for realization of a sustainable virtual marketplace for employees and employers to discover the best fitting job or resource respectively.