We propose a framework to address an important issue in the context of the ongoing adoption of the "Web 2.0" in science and research, often referred to as "Science 2.0" or "Research 2.0". A growing number of people are linked via acquaintances and online social networks such as Twitter1allows indirect access to a huge amount of ideas. These ideas are contained in a massive human information flow. That users of these networks produce relevant data is being shown in many studies. The problem however lies in discovering and verifying such a stream of unstructured data items. Another related problem is locating an expert that could provide an answer to a very specific research question. We are using semantic technologies (RDF, SPARQL), common vocabularies(SIOC, FOAF, SWRC6) and Linked Data (DBpedia, GeoNames, CoLinDa) to extract and mine the data about scientific events out of context of microblogs. Hereby we are identifying persons and organization related to them based on entities of time, place and topic. The framework provides an API that allows quick access to the information that is analyzed by our system. As a proof-of-concept we explain, implement and evaluate such a researcher profiling use case. It involves the development of a framework that focuses on the proposition of researches based on topics and conferences they have in common. This framework provides an API that allows quick access to the analyzed information. A demonstration application: "Researcher Affinity Browser" shows how the API supports developers to build rich internet applications for Research 2.0. This application also introduces the concept "affinity" that exposes the implicit proximity between entities and users based on the content users produced. The usability of a demonstration application and the usefulness of the framework itself are investigated with an explicit evaluation questionnaire. This user feedback led to important conclusions about successful achievements and opportunities to further improve this effort.