-
Sé el primero en recomendar esto
LinkedIn emplea cookies para mejorar la funcionalidad y el rendimiento de nuestro sitio web, así como para ofrecer publicidad relevante. Si continúas navegando por ese sitio web, aceptas el uso de cookies. Consulta nuestras Condiciones de uso y nuestra Política de privacidad para más información.
LinkedIn emplea cookies para mejorar la funcionalidad y el rendimiento de nuestro sitio web, así como para ofrecer publicidad relevante. Si continúas navegando por ese sitio web, aceptas el uso de cookies. Consulta nuestra Política de privacidad y nuestras Condiciones de uso para más información.
Publicado el
At what point does the sheer scale of the available open data itself obstruct openness? Galleries, libraries, archives, and museums are digitising at an increasing pace, and with a growing awareness of the importance of licensing issues and open access. Europeana, long at the forefront of these efforts in the EU context, recently passed the 50millionitem mark in its collections, and other national and international infrastructures are achieving similar milestones. But digitisation and aggregation at this scale mean that modelling richness is in effect often lost. Data heterogeneity across collections, and individual datasets curated to support only certain, very specific, scenarios within them, often combine so that only the very minimal metadata needed for information exchange is available to endusers. Ironically, volume and cataloguing criteria thus potentially combine to put open culture proponents in the position of those intelligence agencies that ‘open’ their miles of archive shelfspace to investigators, but fail to provide an index.
Attention at Europeana and similar organisations has accordingly turned recently to adding structure and semantics to digital open data in a way that makes it more usable, transparent, and comprehensible to endusers. The technologies used and the way they are applied vary with organization, domain, and usecase, and include but are not limited to: semantic enrichment and datamining; personalisation features; crowdsourcing and annotation frameworks; and the creation of knowledge graphs. These technologies all have their own particular advantages and limitations, as will be discussed in brief casestudies. In particular, a strong division is evident between datadriven, empirical approaches such as datamining and usercentric technologies such as crowdsourcing and personalisation. In the ideal case, however, these two tendencies converge on the notion of a ‘user community’ a group, it will be argued, that not only ‘uses’ or ‘consumes’ open data, but shapes it, gives it meaning, and endows it with value.
Sé el primero en recomendar esto
Parece que ya has recortado esta diapositiva en .
Inicia sesión para ver los comentarios