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Figshare, 2018

DOI: 10.6084/m9.figshare.6958610

Springer Verlag (Germany), Communications in Computer and Information Science, p. 157-167

DOI: 10.1007/978-3-319-44066-8_17

Figshare

DOI: 10.6084/m9.figshare.7546568.v1

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A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain

Journal article published in 2016 by Jorge Martinez-Gil ORCID, Alejandra Lorena Paoletti, Klaus-Dieter Schewe
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

We face the complex problem of timely, accurate and mutually satisfactory mediation between job offers and suitable applicant profiles by means of semantic processing techniques. In fact, this problem has become a major challenge for all public and private recruitment agencies around the world as well as for employers and job seekers. It is widely agreed that smart algorithms for automatically matching, learning, and querying job offers and candidate profiles will provide a key technology of high importance and impact and will help to counter the lack of skilled labor and/or appropriate job positions for unemployed people. Additionally, such a framework can support global matching aiming at finding an optimal allocation of job seekers to available jobs, which is relevant for independent employment agencies, e.g. in order to reduce unemployment.