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People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition

Author

Listed:
  • Sabina-Cristiana Necula

    (Department of Accounting, Business Information Systems and Statistics, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700505 Iași, Romania)

  • Cătălin Strîmbei

    (Department of Accounting, Business Information Systems and Statistics, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700505 Iași, Romania)

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.

Suggested Citation

  • Sabina-Cristiana Necula & Cătălin Strîmbei, 2019. "People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition," Sustainability, MDPI, vol. 11(13), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3520-:d:243368
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    References listed on IDEAS

    as
    1. Vasile-Daniel Păvăloaia & Mircea Radu Georgescu & Daniela Popescul & Laura-Diana Radu, 2019. "ESD for Public Administration: An Essential Challenge for Inventing the Future of Our Society," Sustainability, MDPI, vol. 11(3), pages 1-27, February.
    2. Marin FOTACHE, 2016. "Data Processing Languages for Business Intelligence. SQL vs. R," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 20(1), pages 48-61.
    3. Girish Keshav Palshikar & Rajiv Srivastava & Sachin Pawar & Swapnil Hingmire & Ankita Jain & Saheb Chourasia & Mahek Shah, 2019. "Analytics-Led Talent Acquisition for Improving Efficiency and Effectiveness," Springer Proceedings in Business and Economics, in: Arnab Kumar Laha (ed.), Advances in Analytics and Applications, pages 141-160, Springer.
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