Identifying document topics using the Wikipedia category network

Article type: Research Article

Authors: Schönhofen, Peter^;

Affiliations: Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, Budapest 1111, Hungary

Note: [] Support from NKFP projects MOLINGV and Language Miner.

Note: [] E-mail: [email protected].

Abstract: In the last few years the size and coverage of Wikipedia, a community edited, freely available on-line encyclopedia has reached the point where it can be effectively used to identify topics discussed in a document, similarly to an ontology or taxonomy. In this paper we will show that even a fairly simple algorithm that exploits only the titles and categories of Wikipedia articles can characterize documents by Wikipedia categories surprisingly well. We test the reliability of our method by predicting categories of Wikipedia articles themselves based on their bodies, and also by performing classification and clustering on 20 Newsgroups and RCV1, representing documents by their Wikipedia categories instead of (or in addition to) their texts.

Keywords: Wikipedia, ontology, classification, clustering

DOI: 10.3233/WIA-2009-0162

Journal: Web Intelligence and Agent Systems: An International Journal, vol. 7, no. 2, pp. 195-207, 2009