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Similar Text Fragments Extraction for Identifying Common Wikipedia Communities

Author

Listed:
  • Svitlana Petrasova

    (Department of Intelligent Computer Systems, National Technical University “Kharkiv Polytechnic Institute”, 61002 Kharkiv, Ukraine)

  • Nina Khairova

    (Department of Intelligent Computer Systems, National Technical University “Kharkiv Polytechnic Institute”, 61002 Kharkiv, Ukraine)

  • Włodzimierz Lewoniewski

    (Department of Information Systems, Poznan University of Economics and Business, 61-875 Poznan, Poland)

  • Orken Mamyrbayev

    (Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan)

  • Kuralay Mukhsina

    (Department of Informatics, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan)

Abstract

Similar text fragments extraction from weakly formalized data is the task of natural language processing and intelligent data analysis and is used for solving the problem of automatic identification of connected knowledge fields. In order to search such common communities in Wikipedia, we propose to use as an additional stage a logical-algebraic model for similar collocations extraction. With Stanford Part-Of-Speech tagger and Stanford Universal Dependencies parser, we identify the grammatical characteristics of collocation words. With WordNet synsets, we choose their synonyms. Our dataset includes Wikipedia articles from different portals and projects. The experimental results show the frequencies of synonymous text fragments in Wikipedia articles that form common information spaces. The number of highly frequented synonymous collocations can obtain an indication of key common up-to-date Wikipedia communities.

Suggested Citation

  • Svitlana Petrasova & Nina Khairova & Włodzimierz Lewoniewski & Orken Mamyrbayev & Kuralay Mukhsina, 2018. "Similar Text Fragments Extraction for Identifying Common Wikipedia Communities," Data, MDPI, vol. 3(4), pages 1-9, December.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:4:p:66-:d:190245
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    References listed on IDEAS

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    1. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co‐citation clustering using full text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
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    3. David Guy Brizan & Kevin Gallagher & Arnab Jahangir & Theodore Brown, 2016. "Predicting citation patterns: defining and determining influence," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 183-200, July.
    4. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co-citation clustering using full text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
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