CFP: Bridging the Gap between Semantic Web and Web 2.0 March 30, 2007

New kinds of highly popular user-centered applications such as blogs, folksonomies, and wikis, have come to be known as "Web 2.0". The reason for their immediate success is the fact that no specific skills are needed for participating. These new kinds of tools do not only provide data but also generate a lot of weakly structured meta data. One perfect example is tagging. Here users add tags to a resource which can be seen as a kind of meta data. Tags are supposed to describe, from the users point of view, the resource. Such meta data is easy to produce but it lacks any kind of formal grounding used in the Semantic Web.

On the other hand the Semantic Web complements the described bottom-up effort of the Web 2.0 community in a top down manner as, one of its central points is a fixed vocabulary, typed relations and a stronger knowledge representation based on some kind of ontology. Such structure is typically something users have in mind when they provide their information. But for researcher it is hidden in the data and needs to be extracted. Techniques to analyze network structures or weak knowledge representations like those found in the Web 2.0 have a long tradition in different other disciplines, like social network analysis, machine learning or data mining. These kinds of automatic mechanisms are necessary to extract the hidden information and to reveal the structure in a way that the Semantic Web community can benefit from, and thus provide added value to the end user. On the other hand the established way to represent knowledge gained from the unstructured data can be beneficial for the Web 2.0 in that it provides Web 2.0 users with enhanced SemanticWeb features to structure their data.

Our aim is to bridge the gap between the Semantic Web and the upcoming Web 2.0 communities. Since both communities work on network like data structures, analysis methods from different fields of research could form a link between those communities. Techniques can be, but are not limited to, social network analysis, graph analysis, machine learning or data mining methods. By bringing together researchers from different fields, we aim to achieve this goal.

Official Website: http://www.kde.cs.uni-kassel.de/ws/eswc2007/