Towards analytics and collaborative exploration of social and linked media for technology-enchanced learning scenarios

Zerr, Sergej; d'Aquin, Mathieu; Marenzi, Ivana; Taibi, Davide; Adamou, Alessandro and Dietze, Stefan (2014). Towards analytics and collaborative exploration of social and linked media for technology-enchanced learning scenarios. In: PROFILES 2014 Proceedings of the 1st International Workshop on Dataset PROFIling & fEderated Search for Linked Data, CEUR Workshop Proceedings, CEUR-WS.org.

URL: http://www.keystone-cost.eu/profiles2014/

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.

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