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The Knowledge Increase Estimation Framework for Ontology Integration on the Relation Level

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Book cover Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10448))

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Abstract

The task of integration of sets of data or knowledge (regardless the choice of its representation) can be very daunting procedure, requiring a lot of computational resources and time. Authors claim that it is beneficial to develop a formal framework which could be used to estimate the profitability of the integration, ideally even before the integration even occurs. Therefore, a set of algorithms for such estimation of the increase of knowledge concerning relation level of ontology integration is proposed.

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Authors and Affiliations

  1. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Adrianna Kozierkiewicz-Hetmańska & Marcin Pietranik

Authors
  1. Adrianna Kozierkiewicz-Hetmańska
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  2. Marcin Pietranik
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Corresponding author

Correspondence to Adrianna Kozierkiewicz-Hetmańska .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management , Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Kozierkiewicz-Hetmańska, A., Pietranik, M. (2017). The Knowledge Increase Estimation Framework for Ontology Integration on the Relation Level. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-67074-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67073-7

  • Online ISBN: 978-3-319-67074-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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