Next Web: web 3.0, web semántica y el futuro de internet > case

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    Published on 19.8.2014 by Equipo GNOSS

    Case Study: Use of Semantic Web Technologies on the BBC Web Sites

    Publicación en la web de W3C del caso la utilización de tecnologías de web semántica en las páginas web de la BBC. Este caso se publicó en enero de 2010.

    La presentación del caso concluye con los beneficios clave del uso de la tecnología de web semántica:

    "Key Benefits of Using Semantic Web Technology

    • Usability—Making a site around the things people care and think about.
    • User Experience—Having meaningful predicates and granular, addressable resources, so that those resources can be visualised in new ways.
    • User Journeys—Allowing users to make their own journeys across our content. On the BBC /nature, users can start making their own documentaries. They can start on an animal, watch a programme clip, follow a link to a related habitat, read about that habitat and so on…
    • One page per thing—Making our resources part of the Web and therefore linkable and discoverable.
    • Our web site is our API—One URI for both machines and web browsers. Our web site can be used by third parties to create new products, e.g., URIPlayTestTubeTellyFanHubz orChannelography(*).
    • Loosely coupled development—Different teams can work together in a loosely coupled fashion. Each team focuses on their domain of interest."




    Published on 25.2.2013 by Pablo Hermoso de Mendoza González

    Learning Analytics by nature relies on computational information processing activities intended to extract from raw
    data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning
    experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a speci c data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students' enrolment in course modules how
    linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process.