weimo.de

San Francisco (16 of 31)
Originally uploaded by Markus.Weimer

Political correctness gone wrong?
Originally uploaded by Markus.Weimer

Maybe I am just too european to get this: Why would one depict a ‘family’ with caucasian parents and asian children? Would any of my US readers please enlighten me?

(Screenshot of yahoo.com taken today)

Contrast
Originally uploaded by Markus.Weimer

I’m a reviewer of the Special Issue of the Journal of Web Semantics on “Bridging the Gap” – Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0. Here is the call for papers:

Focus of the Special Issue

The last years have seen increasing collaboration of researchers from the Semantic Web, Web 2.0, social network analysis and machine learning communities. Applications that use these research results are achieving economic success. Data now become available that allow researchers to analyze the use, acceptance and evolution of their ideas. Highly popular user-centered applications such as Blogs, social tagging systems, and Wikis have come to be known as “Web 2.0″. A major reason for their immediate success is the high ease of use of new Web 2.0 services. These sites do not only provide data but also generate an abundance of weakly structured metadata. A good example is tagging. Here, users add keywords from an uncontrolled vocabulary, called tags, to a resource. Such metadata are easy to produce, but lack any kind of formal grounding, as used in the Semantic Web. The Semantic Web can complement the bottom-up effort of the Web 2.0 community in a top-down manner. Its central point is a stronger knowledge representation based on some kind of ontology with a fixed vocabulary and typed relations. Such a structure is typically something users have in mind when they provide their information in Web 2.0 systems. However, for further use, this structure is hidden in the data and needs to be extracted. Techniques to analyze network structures or weak knowledge representations as can be found in the Web 2.0 have a long tradition in different other disciplines, like social network analysis, machine learning and data mining. These kinds of automatic mechanisms are necessary to extract the hidden information and to reveal the structure in a way that the end user can benefit from it. Using established methods to represent knowledge gained from unstructured data will also be beneficial for the Web 2.0 in that it provides Web 2.0 users with enhanced Semantic Web features to structure their data. For this special issue, we invite contributions which show how synergies between Semantic Web and Web 2.0 techniques can be successfully used. 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 and data mining methods.

Topics of interest

Topics of interest for this special issue include, but are not limited to:

ontology learning from Web 2.0 data

instance extraction from Web 2.0 systems

analysis of Blogs

discovering social structures and communities

predicting trends and user behaviour

analysis of dynamic networks

using content of the Web for modelling

discovering misuse and fraud

network analysis of social resource sharing systems

analysis of folksonomies and other Web 2.0 data structures

analysis of Web 2.0 applications and their data

deriving profiles from usage

personalized delivery of news and journals

Semantic Web personalization

Semantic Web technologies for recommender systems

ubiquitous data mining in Web (2.0) environment

applications

In accordance with the focus of the journal, the relatedness of your submission to the Semantic Web will be an important evaluation criterion.