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East European Conference on Advances in Databases and Information Systems

ADBIS 2015: Advances in Databases and Information Systems pp 245–258Cite as

SeeCOnt: A New Seeding-Based Clustering Approach for Ontology Matching

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9282))

Abstract

Ontology matching plays a crucial role to resolve semantic heterogeneities within knowledge-based systems. However, ontologies contain a massive number of concepts, resulting in performance impediments during the ontology matching process. With the increasing number of ontology concepts, there is a growing need to focus more on large-scale matching problems. To this end, in this paper, we come up with a new partitioning-based matching approach, where a new clustering method for partitioning concepts of ontologies is introduced. The proposed method, called SeeCOnt, is a seeding-based clustering technique aiming to reduce the complexity of comparison by only using clusters’ seed. In particular, SeeCOnt first identifies and determines the seeds of clusters based on the highest ranked concepts using a distribution condition, then the remaining concepts are placed into the proper cluster by defining and utilizing a membership function. The SeeCOnt method can improve the memory consuming problem in the large-scale matching problem, as well as it increases the matching quality. The experimental evaluation shows that SeeCOnt, compared with the top ten participant systems in OAEI, demonstrates acceptable results.

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Notes

  1. 1.

    http://www.cs.ox.ac.uk/isg/projects/SEALS/oaei/2014/.

  2. 2.

    https://jena.apache.org/.

  3. 3.

    http://ws.nju.edu.cn/falcon-ao.

  4. 4.

    http://alignapi.gforge.inria.fr.

  5. 5.

    http://oaei.ontologymatching.org.

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Acknowledgments

A. Algergawy’work is partly funded by DFG in the INFRA1 project of CRC AquaDiva.

Author information

Authors and Affiliations

  1. Institute of Computer Science, Friedrich Schiller University of Jena, Jena, Germany

    Alsayed Algergawy

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

    Alsayed Algergawy

  3. Department of Computer Engineering, University of Science and Culture, Tehran, Iran

    Samira Babalou, Mohammad J. Kargar & S. Hashem Davarpanah

Authors
  1. Alsayed Algergawy
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  2. Samira Babalou
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  3. Mohammad J. Kargar
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  4. S. Hashem Davarpanah
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Corresponding author

Correspondence to Alsayed Algergawy .

Editor information

Editors and Affiliations

  1. Poznan University of Technology, Poznán, Poland

    Morzy Tadeusz

  2. INRIA, Montpellier, France

    Patrick Valduriez

  3. Teleport 2, LIAS/ISAE-ENSMA, Poitiers, France

    Ladjel Bellatreche

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Algergawy, A., Babalou, S., Kargar, M.J., Davarpanah, S.H. (2015). SeeCOnt: A New Seeding-Based Clustering Approach for Ontology Matching. In: Tadeusz, M., Valduriez, P., Bellatreche, L. (eds) Advances in Databases and Information Systems. ADBIS 2015. Lecture Notes in Computer Science(), vol 9282. Springer, Cham. https://doi.org/10.1007/978-3-319-23135-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-23135-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23134-1

  • Online ISBN: 978-3-319-23135-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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