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A Latent Semantic Indexing-Based Approach to Determine Similar Clusters in Large-scale Schema Matching

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New Trends in Databases and Information Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 241))

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Abstract

Schema matching plays a central role in identifying the semantic correspondences across shared-data applications, such as data integration. Due to the increasing size and the widespread use of XML schemas and different kinds of ontologies, it becomes toughly challenging to cope with large-scale schema matching. Clustering-based matching is a great step towards more significant reduction of the search space and thus improved efficiency. However, methods used to identify similar clusters depend on literally matching terms. To improve this situation, in this paper, a new approach is proposed which uses Latent Semantic Indexing that allows retrieving the conceptual meaning between clusters. The experimental evaluations show encourage results towards building efficient large-scale matching approaches.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Otto-von-Guericke University, 39106, Magdeburg, Germany

    Alsayed Algergawy & Gunter Saake

  2. Department of Computer Engineering, Tanta University, Tanta, Egypt

    Alsayed Algergawy & Amany Sarhan

  3. Department of Computer Engineering, Mansoura University, Mansoura, Egypt

    Seham Moawed & Ali Eldosouky

Authors
  1. Seham Moawed
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  2. Alsayed Algergawy
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  3. Amany Sarhan
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  4. Ali Eldosouky
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  5. Gunter Saake
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica Bioingegneria, Robotica e, Università di Genova, Genova, Italy

    Barbara Catania

  2. Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy

    Tania Cerquitelli

  3. Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy

    Silvia Chiusano

  4. Dipartimento di Informatica, Bioingegneria, Robotica e, Università di Genova, Genova, Italy

    Giovanna Guerrini

  5. Cloudera, Inc., California,, California, USA

    Mirko Kämpf

  6. Faculty of Informatics, Technische Universität München, Garching, Germany

    Alfons Kemper

  7. Dept. of Analytical Information Systems, Saint Petersburg University, Saint Petersburg, Russia

    Boris Novikov

  8. Dipartimento di Ingegneria e Scienza, dell’Informazione, ItalyUniversità di Trento, Povo, TN,, Italy

    Themis Palpanas

  9. Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Praha, Praha, Czech Republic

    Jaroslav Pokorný

  10. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Athena Vakali

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Moawed, S., Algergawy, A., Sarhan, A., Eldosouky, A., Saake, G. (2014). A Latent Semantic Indexing-Based Approach to Determine Similar Clusters in Large-scale Schema Matching. In: Catania, B., et al. New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-01863-8_29

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  • DOI: https://doi.org/10.1007/978-3-319-01863-8_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01862-1

  • Online ISBN: 978-3-319-01863-8

  • eBook Packages: EngineeringEngineering (R0)

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