Nothing Special   »   [go: up one dir, main page]

Skip to main content

Classification Methods of Text Documents Using Ontology Based Approach

  • Chapter
  • First Online:
Advances in Intelligent Systems and Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 512))

  • 1801 Accesses

Abstract

This article discusses an approach to classification of text documents using ontological approach. The method of text documents categorization based on metrics, which uses the rubric ontology specificity, is built.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook
USD 15.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Andreev, A.M., Berezkyn, D.V., Syuzev, V.V., Shabanov, V.Y.: Models and methods of automatic classification of text sudden-dock. Vestn. Bauman. Avg. Pryborostroenye. Publishing House of Bauman, Moscow, No. 3, pp. 45–51 (2003)

    Google Scholar 

  2. Lytvyn, V.: Multiagent decision support system based on precedent and use adaptive ontology. Electronics, Informatics, Management. Zaporizhzhia, No. 2 (21), pp. 120–126 (2009)

    Google Scholar 

  3. Kruglov, V.V., Borysov, V.V.: Iskusstvennyye neyronnyye seti. Teoriya i praktika. M.: Hotline—Telecom (2001)

    Google Scholar 

  4. Sowa, J.: Conceptual graphs for a database interface. IBM J. Res. Dev. 20(4), 336–357 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  5. Darewych, R.R., Dosyn, D.H., Lytvyn, V.V., Nazarchuk, Z.T.: Assessment of the similarity of text documents based on the weight of items using information knowledge base. Artif. Intell. Donetsk 3, 500–509 (2006)

    Google Scholar 

  6. Thulasiraman, K., Swamy, M.N.: Graphs: theory and algorithms. John Wiley & Sons (2011)

    Google Scholar 

  7. Lytvyn, V., Darevych, R.R., Dosyn, D.H., Shkutyak, N.V.: Design of intelligent agents making decisions in the space of lake-NAC Ontology-based. Artif. Intell. Donetsk-Katsively 2, 100–104 (2010)

    Google Scholar 

  8. Dosyn, D.H., Lytvyn, V.V., Nikolskyy, Y.V., Pasichnyk, V.V.: Intelligent System Based on Ontologies. Civilization, Lviv (2009)

    Google Scholar 

  9. Link Grammar Homepage. Access mode: http://www.link.cs.cmu.edu/link/

  10. Biggs, N., Lloyd, E., Wilson, R.: Graph Theory, pp. 1736–1936. Oxford University Press, Oxford (1986)

    Google Scholar 

  11. Bondy, J.A., Murty, U.S.R.: Graph Theory. Springer. ISBN 978-1-84628-969-9 (2008)

    Google Scholar 

  12. Alan, R.J.: A machine-oriented logic based on the resolution principle. J. ACM (JACM) 12(1), 23–41 (1965)

    Article  MathSciNet  Google Scholar 

  13. Ramı, J., Vlach, M.: Pareto-optimality of compromise decisions. Fuzzy Sets Syst. 129(1), 119–127 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Poirriez, V., Yanev, N., Andonov, R.: A hybrid algorithm for the unbounded knapsack problem. Disc. Opt. 6(1), 110–124 (2009)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vasyl Lytvyn .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Lytvyn, V., Vysotska, V., Veres, O., Rishnyak, I., Rishnyak, H. (2017). Classification Methods of Text Documents Using Ontology Based Approach. In: Shakhovska, N. (eds) Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-45991-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45991-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45990-5

  • Online ISBN: 978-3-319-45991-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics