the performance of FolkRank-based search. Further, we demonstrate the benefits of TagMe! tagging facets for learning semantics within folksonomies. 1

MOOCs, developing countries, connectivism, online learning Massively Open Online Courses (MOOCs) are a recent but hugely popular phenomenon in the online learning world. They are hailed by many as a solution for the developing world’s lack of access to education because MOOCs can provide learning

KB-N is a web-accessible searchable Knowledge Bank comprising A) a parallel corpus of quality assured and calibrated English and Norwegian text drawn from economic-administrative knowledge domains, and B) a domain-focused database representing that knowledge universe in terms of defined concepts and their respective bilingual terminological entries. A central mechanism in connecting A and B is an algorithm for the automatic extraction of term candidates from aligned translation pairs on the basis of linguistic, lexical and statistical filtering (first ever for Norwegian). The system is designed and programmed by Paul Meurer at Aksis (UiB). An important pilot application of the term base is subdomain and collocations based word-sense disambiguation for LOGON, a system for Norwegian-to-English MT currently being developed.

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doi:10.1251/bpo94 A method for rapidly screening functionality of actin mutants and

Measuring auditory selective attention using frequency

HIV i-Base is a London-based HIV treatment activist organization. HIV i-Base works in the United Kingdom and internationally to ensure that people living with HIV are actively engaged in their own treatment and medical care and are included in policy discussions about HIV treatment recommendations and access. www.i-base.info

In this paper we present ranking algorithms for folksonomy systems that exploit additional contextual information attached to tag assignments available. We evaluate the algorithms in the TagMe! system, a tagging front-end for Flickr, and show that our algorithms, which exploit categories, spatial

and categories to DBpedia URIs to clearly define the meaning. In this work we discuss the differences between tags and categories and show how both facets can be applied to learn semantic relations between concepts referenced by tags and categories. We also expose the benefits of the visual (spatial) context

and categories to DBpedia URIs to clearly define the meaning. In this work we discuss the dif-ferences between tags and categories and show how both facets can be applied to learn semantic relations between concepts referenced by tags and categories. We also expose the benefits of the visual (spatial) context