Exploration of Document Classification with Linked Data and PageRank

  • Martin Dostal
  • Michal Nykl
  • Karel Ježek
Part of the Studies in Computational Intelligence book series (SCI, volume 511)


In this article, we would like to present a new approach to classification using Linked Data and PageRank. Our research is focused on classification methods that are enhanced by semantic information. The semantic information can be obtained from ontology or from Linked Data. DBpedia was used as a source of Linked Data in our case. The feature selection method is semantically based so features can be recognized by non-professional users as they are in a human readable and understandable form. PageRank is used during the feature selection and generation phase for the expansion of basic features into more general representatives. This means that feature selection and PageRank processing is based on network relations obtained from Linked Data. The discovered features can be used by standard classification algorithms. We will present promising results that show the simple applicability of this approach to two different datasets.


Feature Selection Semantic Information Link Data Feature Selection Method Basic Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Berners-Lee, T.: Linked Data - Design Issues. Online document (2006), (Cited January 12, 2013)
  2. 2.
    Bloehdorn, S., Hotho, A.: Boosting for Text Classification with Semantic Features. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds.) WebKDD 2004. LNCS (LNAI), vol. 3932, pp. 149–166. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Brine, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
  4. 4.
    Cohen, W., Singer, Y.: Context-sensitive learning methods for text categorization. In: Proceedings of the ACM SIGIR 1996 (1996)Google Scholar
  5. 5.
    DBPedia, (Cited January 12, 2013)
  6. 6.
    de Melo, G., Siersdorfer, S.: Multilingual text classification using ontologies. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECiR 2007. LNCS, vol. 4425, pp. 541–548. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Gabrilovich, E., et al.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: Proceedings of the IJCAI 2007, Hyderabad, India, pp. 1606–1611 (2007)Google Scholar
  8. 8.
    Jaffri, A., Glaser, H., Millard, I.: URI Disambiguation in the Context of Linked Data. In: Proceedings of the LDOW 2008, Beijing, China (2008)Google Scholar
  9. 9.
    Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, 20 News groups dataset, pp. 331–339 (1995)Google Scholar
  10. 10.
    Langville, A.N., Meyer, C.D.: Google’s PageRank and Beyond: The Science of Search Engine Ranking. Princeton University Press, Princeton (2006)Google Scholar
  11. 11.
    Ma, N., et al.: Bringing PageRank to the citation analysis. Proceedings of the Information Processing & Management 44(2), 800–810 (2008)CrossRefGoogle Scholar
  12. 12.
    Ramakrishnanan, G., Bhattacharyya, P.: Text Representation with WordNet Synsets using Soft Sense Disambiguation. In: Proceedings of the 8th NLDB, Burg, Germany (2003)Google Scholar
  13. 13.
    Salton, G.: The SMART Retrieval System. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  14. 14.
    Schapire, R., Singer, Y.: BoosTexter: A boosting-based system for text categorization. In: Machine Learning, pp. 135–168 (1999)Google Scholar
  15. 15.
    Strube, M., Ponzetto, S.P.: WikiRelate! Computing semantic relatedness using Wikipedia. In: Proceedings of the AAAI 2006, Boston, USA, pp. 1419–1424 (2006)Google Scholar
  16. 16.
    Wang, W., Do, D.B., Lin, X.: Term Graph Model for Text Classification. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 19–30. Springer, Heidelberg (2005)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.NTIS - New Technologies for the Information Society, Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic
  2. 2.Department of Computer Science and Engineering, Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic

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