http://www.cnr.it/ontology/cnr/individuo/prodotto/ID294994
Towards Analytics and Collaborative Exploration of Social and linked Media for Technology-Enchanced Learning Scenarios (Contributo in atti di convegno)
- Type
- Label
- Towards Analytics and Collaborative Exploration of Social and linked Media for Technology-Enchanced Learning Scenarios (Contributo in atti di convegno) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Alternative label
Sergej Zerr, Mathieu D'Aquin, Ivana Marenzi, Davide Taibi, Adamou Alessandro, Stefan Dietze (2014)
Towards Analytics and Collaborative Exploration of Social and linked Media for Technology-Enchanced Learning Scenarios
in 1st International Workshop on Dataset PROFIling & fEderated Search for Linked Data co-located with the 11th Extended Semantic Web Conference, PROFILES@ESWC 2014, Anissaras, Crete, Greece, May 26, 2014
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Sergej Zerr, Mathieu D'Aquin, Ivana Marenzi, Davide Taibi, Adamou Alessandro, Stefan Dietze (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://ceur-ws.org/Vol-1151/paper3.pdf (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- L3S Research Center
Knowledge Media Institute, The Open University
Italian National Research Concil, Institute of Educational Technologies (literal)
- Titolo
- Towards Analytics and Collaborative Exploration of Social and linked Media for Technology-Enchanced Learning Scenarios (literal)
- Abstract
- Social Web applications such as \"Flickr\", \"Youtube\" and \"Slideshare\" offer a vast body of multimedial knowledge, discoverable
through the appropriate search interfaces and API's. This extensive information source, however, is largely unstructured and the available metadata is typically limited to title, tags and description for a resource. On the other hand, Linked Web Data is both structured and well described through a variety of metadata. Combining those sources opens promising direction for knowledge discovery and, at the same time, new challenges for collaborative searching in various Technology-Enchanced
Learning Scenarios. In this paper, we explore how to support (collaborative) search in such scenarios through an initial analysis of the Web data landscape and introduce early results from efforts on exploiting Linked Data techniques to solve critical issues in this context. (literal)
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