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On the Use of Semantic Technologies to Support Education, Mobility and Employability

  • Valentina Gatteschi
  • Fabrizio Lamberti
  • Claudio Demartini
Part of the Studies in Computational Intelligence book series (SCI, volume 418)

Abstract

The technological development that has taken place over recent years has affected the way that the Web is exploited by users. In fact, a user is no longer seen as a mere observer; instead, he or she actively takes part in the production of new knowledge, which is then made available to other users. In this scenario, which is characterized by ever-growing information, new approaches (e.g., based on semantic technologies) for processing large amounts of content must be developed. Other sectors, including education and learning, as well as job seeking and hiring activities, could benefit from the exploitation of tools that can be developed within the Semantic Web initiative. As a consequence of the use of semantic technologies, learners could find more efficient training paths that provide them with their missing areas of competence and training institutes could analyze and modify existing qualifications according to a market’s requirements, while companies could effectively identify the best candidates for a given job, not only within national boundaries but also worldwide. This study aims to present an overview of recent research in the field of semantic technologies applied to education and job-seeking contexts. Issues that are related to students’ and workers’ mobility, job seeking and hiring and the improvement of qualification offers will be analyzed and compared, by distinguishing three fields of research: knowledge base creation, the development of strategies for the integration of heterogeneous systems and the definition of inference rules, and the identification of methodologies for the visualization of qualification outcomes and curricula.

Keywords

Unify Modeling Language Inference Rule Semantic Technology Bologna Declaration Training Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Valentina Gatteschi
    • 1
  • Fabrizio Lamberti
    • 1
  • Claudio Demartini
    • 1
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTorinoItaly

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