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International Conference on Applications of Natural Language to Information Systems

NLDB 2017: Natural Language Processing and Information Systems pp 435–438Cite as

Improving Document Classification Effectiveness Using Knowledge Exploited by Ontologies

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10260))

Abstract

In this paper, we propose a new document classification model which utilizes background knowledge gathered by ontologies for document representation. A document is represented using a set of ontology concepts that are acquired by exact matching technique and through identification and extraction of new terms which can be semantically related to these concepts. In addition, a new concept weighting scheme composed of concept relevance and importance is employed by the model to compute weight of concepts. We conducted experiments to test the model and the obtained results showed that a considerable improvement of classification performance is achieved by using our proposed model.

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References

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Author information

Authors and Affiliations

  1. Information Technology and Electrical Engineering, Norwegian University of Technology and Science, Gjøvik, Norway

    Zenun Kastrati & Sule Yildirim Yayilgan

Authors
  1. Zenun Kastrati
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  2. Sule Yildirim Yayilgan
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Corresponding author

Correspondence to Zenun Kastrati .

Editor information

Editors and Affiliations

  1. Erasmus University Rotterdam, Rotterdam, The Netherlands

    Flavius Frasincar

  2. University of Liège , Liège, Belgium

    Ashwin Ittoo

  3. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Le Minh Nguyen

  4. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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Kastrati, Z., Yayilgan, S.Y. (2017). Improving Document Classification Effectiveness Using Knowledge Exploited by Ontologies. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_52

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  • DOI: https://doi.org/10.1007/978-3-319-59569-6_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59568-9

  • Online ISBN: 978-3-319-59569-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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