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YAM++ : A Multi-strategy Based Approach for Ontology Matching Task

  • Conference paper
Knowledge Engineering and Knowledge Management (EKAW 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7603))

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Abstract

In this paper, we present the capability of our ontology matching tool YAM++. We show that YAM++ is able to discover mappings between entities of given two ontologies by using machine learning approach. Besides, we also demonstrate that if the training data are not available, YAM++ can discover mappings by using information retrieval techniques. Finally, we show that YAM++ is able to deal with multi-lingual ontologies matching problem.

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References

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Author information

Authors and Affiliations

  1. INRIA, LIRMM, Université Montpellier 2, Montpellier, France

    DuyHoa Ngo & Zohra Bellahsene

Authors
  1. DuyHoa Ngo
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  2. Zohra Bellahsene
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Editor information

Editors and Affiliations

  1. Vrije Universiteit, Amsterdam, The Netherlands

    Annette ten Teije

  2. Institute of Computer Science and Business Informatics, University of Mannheim, Germany

    Johanna Völker  & Heiner Stuckenschmidt  & 

  3. Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland

    Siegfried Handschuh

  4. Knowledge Media Institute, The Open University, Milton Keynes, UK

    Mathieu d’Acquin  & Andriy Nikolov  & 

  5. Institut de Recherche en Informatique, Université de Toulouse, 118, route de Narbonne, 31062, Toulouse Cedex 4, France

    Nathalie Aussenac-Gilles

  6. Université de Toulouse, France

    Nathalie Hernandez

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© 2012 Springer-Verlag Berlin Heidelberg

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Ngo, D., Bellahsene, Z. (2012). YAM++ : A Multi-strategy Based Approach for Ontology Matching Task. In: ten Teije, A., et al. Knowledge Engineering and Knowledge Management. EKAW 2012. Lecture Notes in Computer Science(), vol 7603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33876-2_38

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  • DOI: https://doi.org/10.1007/978-3-642-33876-2_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33875-5

  • Online ISBN: 978-3-642-33876-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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