Researcher Profiling based on Semantic Analysis in Social Networks
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We propose a framework to address an important challenge in the context of the ongoing adoption of the “Web 2.0” in science and research, often referred to as “Research 2.0”. Microblogging is ...
We propose a framework to address an important challenge in the context of the ongoing adoption of the “Web 2.0” in science and research, often referred to as “Research 2.0”. Microblogging is one of the trends with increasing leverage. The challenge in this thesis is to connect users of microblogging services such as Twitter based on specific common entities that are representative and truly matter to them. We investigated the possibilities of using social data for locating an expert who shares a very specific research topic. To enrich and verify this social data we link such content to existing open data provided by the online community. We are using semantic technologies (RDF ,SPARQL), com- mon ontologies (SIOC, FOAF, DublinCore, SWRC) and Linked Data (DBpedia, GeoNames, CoLinDa) to extract and mine the data about scientific conferences out of context of microblogs. We are identifying users related to each other based on entities such as topics (tags), events, time, locations and persons (mentions). 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 intro- duces 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 question- naire. This user feedback lead to important conclusions about successful achievements and opportunities to further improve this effort.
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