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Identifying research schools using enriched bibliographical metadata

: Vahdati, Sahar; Behrend, Andreas; Lange, Christoph


Association for Computing Machinery -ACM-:
I-KNOW 2014, 14th International Conference on Knowledge Technologies and Data-driven Business. Proceedings : Graz, Austria, September 16-19, 2014
New York: ACM, 2014
ISBN: 978-1-4503-2769-5
Art. 39, 4 S.
International Conference on Knowledge Technologies and Data-Driven Business (i-KNOW) <14, 2014, Graz>
Fraunhofer IAIS ()

Most scientific publications are easily accessible nowadays, but their accessibility alone does not yet provide researchers with all services they really need. Researchers investigating a new topic still spend a lot of time searching for the most relevant publications, persons or groups inside the respective community. Even experienced members of a community may know who to ask for explaining new techniques or what conference's proceedings to read, but still the relevant information they are interested in is not accessible at a glance. We claim that comprehensive bibliographical analysis services will help both new and experienced researchers to gain an overview of relevant publications, persons or groups within their community more easily. In this paper we present an approach to identifying research schools and their influence on each other, by quantitatively analysing citation graphs and Metadata of publications. Additionally, an outlook is provided towards automating this kind of analysis using linked data technology.