A smart approach for matching, learning and querying information from the human resources domain
|J. Martinez Gil, L. Paoletti, K. Schewe. A smart approach for matching, learning and querying information from the human resources domain. volume 637, pages 157-167, DOI 10.1007/978-3-319-44066-8_17, 8, 2016.|
|Buch||New Trends in Databases and Information Systems - Proc. ADBIS 2016 Short Papers and Workshops|
|Serie||Communications in Computer and Information Science|
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. 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 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.