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.
Osborne, F., Peroni, S., Motta, E. (2014). Clustering citation distributions for semantic categorization and citation prediction. CEUR-WS.
Clustering citation distributions for semantic categorization and citation prediction
PERONI, SILVIO;
2014
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
