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The growth of data produced for example by IoT devices has playing a major role in developing healthcare applications able to effectiveness handle the vast amount of information. The challenge lies in representing volumes of data, integrating and understanding their various formats and sources. Cognitive computing systems offer promise for analysing, accessing, integrating, and investigating data in order to improve outcomes across many domains, including healthcare. This paper presents an ontology-based system for the eHealth domain. It provides semantic interoperability among heterogeneous IoT fitness and wellness devices and facilitates data integration and sharing. The novelty of the proposed approach lies in exploiting semantic web technologies to explicitly describe the meaning of sensor data and define a common communication strategy for information representation and exchange., Journal of e-Learning and Knowledge Society, Vol 14 No 1 (2018): Journal of e-Learning and Knowledge Society - Focus on: New trends, challenges and perspectives on healthcare cognitive computing: from information extraction to healthcare analytics

The Web provides not only several data sources with useful and relevant information with e-Learning purposes, but also information that is not easy to retrieve. The web of linked data is a repository based on semantic technologies. Several researchers have been oriented to this kind of interoperable e-Learning repositories and establish that the Linked Data approach has the potential to fulfill the e-Learning vision of Web-scale interoperability of e-Learning resources as well as highly personalized and adaptive e-Learning applications. The paper presents an automatic concept extraction system used to improve personalized searching framework. It could be considered as one possible instance of a more general concept concerning the transition from the Document Web to the Document/Data Web and the consequent managing of these immense volumes of data., Journal of e-Learning and Knowledge Society, Vol 8, No 2 (2012): Journal of e-Learning and Knowledge Society: Focus on: Semantic Web and e-Learning

Journal of e-Learning and Knowledge Society, Vol 10, No 1 (2014): Focus on: Recommender Systems for Technology-Supported Learning

In this paper we introduce an update of our work on developing a distance learning environment based on a collaborative bookmark management system approach. We introduce EasyInfo, an hybrid recommender architecture extension, which processes resources to extract concepts (not just words) from the documents using semantic capabilities. Then, the classifi cation, recommendation and sharing phases take advantage of the word senses to classify, retrieve and suggest documents with high semantic relevance with respect to the student and resource models., Journal of e-Learning and Knowledge Society, Vol 2, No 3 (2006): Journal of e-Learning and Knowledge Society