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Combined Method for Integration of Heterogeneous Ontology Models for Big Data Processing and Analysis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 573))

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Abstract

In the given paper a combined method for integration of heterogeneous ontology for big data processing and analysis is proposed. This allows perform semantic search through heterogeneous information resources, represented by different ontologies. The fundamental difference of the proposed approach is that it allows obtaining optimal weights on the basis of which the optimal alignment of ontologies is carried out. Performed calculations validate the productivity of the proposed method.

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Authors and Affiliations

  1. Autonomous Federal State Institution of Higher Education, Southern Federal University, Rostov, Russia

    Viktor Kureychik & Alexandra Semenova

Authors
  1. Viktor Kureychik
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  2. Alexandra Semenova
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Corresponding author

Correspondence to Alexandra Semenova .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zdenka Prokopova

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Petr Silhavy

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Kureychik, V., Semenova, A. (2017). Combined Method for Integration of Heterogeneous Ontology Models for Big Data Processing and Analysis. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-57261-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57260-4

  • Online ISBN: 978-3-319-57261-1

  • eBook Packages: EngineeringEngineering (R0)

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