People analytics of semantic web human resource résumés for sustainable talent acquisition

Abstract

The purpose of this study was to define a data science architecture for talent acquisition. The approach was to propose analytics that derive data. The originality of this paper consists in proposing an architecture to work within the process of obtaining semantically enriched data by using data science and Semantic Web technologies. We applied the proposed architecture and developed a case study-based prototype that uses analytics techniques for résumé data integrated with Linked Data technologies. We conducted a case study to identify skills by applying classification via regression, k-nearest neighbors (k-NN), random forest, naïve Bayes, support vector machine, and decision tree algorithms to résumé data that we previously described with terms from publicly available ontologies. We labeled data from résumés using terms from existing human resource ontologies. The main contribution is the extraction of skills from résumés and the mining of data that was previously described with the Semantic Web.