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Clustering citation distributions for semantic categorization and citation prediction

Osborne, Francesco; Peroni, Silvio and Motta, Enrico (2014). Clustering citation distributions for semantic categorization and citation prediction. In: I4th Workshop on Linked Science 2014— Making Sense Out of Data (LISC2014) , 19-23 October 2014, Riva Del Garda, Trentino, Italy (Forthcoming).

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Abstract

In this paper we present i) an approach for clustering authors according to their citation distributions and ii) an ontology, the Bibliometric Data Ontology, for supporting the formal representation of such clusters. This method allows the formulation of queries which take in consideration the citation behaviour of an author and predicts with a good level of accuracy future citation behaviours. We evaluate our approach with respect to alternative solutions and discuss the predicting abilities of the identified clusters.

Item Type: Conference or Workshop Item
Extra Information: Collocated with the 13th International Semantic Web Conference (ISWC 2014), 19-23 October 2014
Keywords: Semantic Web; research data; bibliometric data; expert search; hierarchical clustering; data mining; OWL; RDF; SPARQL; BiDO
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 40784
Depositing User: Francesco Osborne
Date Deposited: 04 Sep 2014 12:20
Last Modified: 25 Sep 2017 12:56
URI: http://oro.open.ac.uk/id/eprint/40784
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