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Introducing Artificial Neural Network in Ontologies Alignement Process

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Book cover New Trends in Databases and Information Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 185))

Abstract

During automated/semi-automated alignment across myriad ontologies, different similarity measures of different categories such as string, linguistic, and structural based similarity measures, contribute each to some extend to alignment results. A weights vector must, therefore, be assigned to these similarity measures, if a more accurate and meaningful alignment result is favored. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics and/or prior domain knowledge. In this paper, we take an artificial neural network approach to learn and adjust these weights, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. XMap++ is applied to benchmark tests at OAEI campaign 2010. Results show that neural network boosts the performance in most cases, and that the proposed novel approach is competitive with top-ranked system.

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Author information

Authors and Affiliations

  1. LabGED, Computer Science Department, University Badji Mokhtar, Po-Box 12, 23000, Annaba, Algeria

    Warith Eddine Djeddi & Mohamed Tarek Khadir

Authors
  1. Warith Eddine Djeddi
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  2. Mohamed Tarek Khadir
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Corresponding author

Correspondence to Warith Eddine Djeddi .

Editor information

Editors and Affiliations

  1. , Department of Computer Science, Eindhoven University of Technology, Eindhoven, 5600, Netherlands

    Mykola Pechenizkiy

  2. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, Poznan, 60-965, Poland

    Marek Wojciechowski

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Djeddi, W.E., Khadir, M.T. (2013). Introducing Artificial Neural Network in Ontologies Alignement Process. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_17

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  • DOI: https://doi.org/10.1007/978-3-642-32518-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32517-5

  • Online ISBN: 978-3-642-32518-2

  • eBook Packages: EngineeringEngineering (R0)

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