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Elsevier announces winners of 2009 Semantic Web Challenge -

STM publisher Elsevier, Netherlands, has announced the winners of the 2009 Semantic Web Challenge, which took place at the International Semantic Web Conference held in Washington, D.C. A jury consisting of eleven leading experts from both academia and industry awarded the four best applications with cash prizes of 2750 Euro in total, sponsored by Elsevier.

The semantic web aims to extract meaning and intelligence from the net. The Semantic Web Challenge has been set up to have new applications compete with each other for highly coveted awards. Over the last seven years, the Challenge has attracted over 120 submissions, and each of these have been carefully evaluated on scientific and technological prowess, as well as its practical applicability to solve real world issues. The continuing maturity of the tools and components used to build applications has resulted in increasingly more compelling demonstrations.

The 2009 Semantic Web Challenge was organised by Peter Mika of Yahoo! Research and Chris Bizer of Freie Universität Berlin, and covered two categories – ‘Open Track’ and ‘Billion Triples Track.’ Open Track requires that the applications utilise the semantics (meaning) of data and that they have been designed to operate in an open web environment, while the Billion Triples Track focuses on dealing with very large data sets of low quality commonly found on the web.

The winners of the 2009 Open Track were Chintan Patel, Sharib Khan and Karthik Gomadam from Applied Informatics, Inc for ‘TrialX’. TrialX enables finding new treatments by intelligently matching patients to clinical trials using advanced medical onthologies to combine several electronic health records with user generated information.

The Billion Triples Track was won by ‘Scalable Reduction’ by Gregory Todd Williams, Jesse Weaver, Medha Atre, and James A. Hendler (Rensselaer Polytechnic Institute, USA). The entry showed how massive parallelisation can be applied to quickly clean and filter large amounts of RDF data.

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