Based upon findings and results from our recent research (De Vocht et al., 2011) we propose a generic framework concept for researcher profiling with appliance to the areas of ”Science 2.0” and ”Research 2.0”. Intensive growth of users in social networks, such as Twitter generated a vast amount of information. It has been shown in many previous works that social networks users produce valuable content for profiling and recommendations (Reinhardt et al., 2009; Java et al., 2007; De Vocht et al., 2011). Our research focuses on identifying and locating experts for specific research area or topic. In our approach we apply semantic technologies like (RDFb, SPARQLc), common vocabularies (SIOCd, FOAFe, MOATf, Tag Ontologyg) and Linked Datah (GeoNamesi, COLINDAj) (Berners-Lee, 2006; Bizer et al., 2012) .

This short survey represents a first step towards identifying relevant enterprise search engines as possible key enablers for challenges related to Future Workplace like knowledge transfer and individual and organisational information management inside an enterprise collaboration cycle. In the early period of our research we want to deliver a short overview on current state of the art solutions that can contribute to the idea of Future Workplace. We summarised in our literature study most significant parameters from current research on the topic of semantic and enterprise search. Information was collected via product sheets and white paper of the vendors as well using previous studies and accessible information at the web. Four most advanced solutions in this area has been evaluated. We used them to check their appliance for Future Workplace trends and are aiming at expansion of our review for additional solutions in the future.

This work reports about the preliminary results and ongoing research based upon profiling collaborative learning groups of persons within the social micro-blogging platforms like Twitter1 that share potentially common interests on special topic. Hereby the focus is held on spontaneously initiated collaborative learning in Social Media and detection of collaborative learning groups based upon their communication dynamics. Research questions targeted to be answered are: are there any useful data mining algorithms to fulfill the task of pre-selection and clustering of users in social networks, how good do they perform, and what are the metrics that could be used for detection and evaluation in the realm of this task. Basic approach presented here uses as preamble hypothesis that users and their interests in Social Networks can be identified through content generated by them and content they consume. Special focus is held on topic oriented approach as least common bounding point. Those should be also the basic criteria used to detect and outline the learning groups. The aim of this work is to deliver first scientific pre-work for successfully implementation of recommender systems using social network metrics and content features of social network users for the purposes of better learning group communication and information consumption.