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Intelligent Systems for Knowledge Management pp 197–227Cite as

Effective Backbone Techniques for Ontology Integration

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 252))

Abstract

Most previous research on ontology integration has focused on similarity measurements between ontological entities, e.g., lexicons, instances, schemas and taxonomies, resulting in high computational costs due to the need to consider all possible pairs between two given ontologies. In this chapter, we present a novel approach to reduce computational complexity in ontology integration. Thereby, we present an enriched ontology model for a formal ontological analysis that enables us to build ontologies with a clean and untangled taxonomic structure. The importance and types of concepts, for priority matching and direct matching between concepts, respectively are proposed. Identity-based similarity is computed, which enables us to avoid comparisons of all properties related to each concept, while matching between concepts. The problem of conflict in ontology integration has initially been explored on the instance-level and concept-level. This is useful to avoid many cases of mismatching.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Inha University, Korea

    Trong Hai Duong & Geun Sik Jo

  2. Institute of Informatics and Engineering, Wroclaw University of Technology, Poland

    Ngoc Thanh Nguyen

Authors
  1. Trong Hai Duong
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  2. Ngoc Thanh Nguyen
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  3. Geun Sik Jo
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str.Wyb.Wypsianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Faculty of Management and Economics, Gdansk University of Technology, Str.Narutowicza 11/12, 80-233, Gdansk, Poland

    Edward Szczerbicki

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Duong, T.H., Nguyen, N.T., Jo, G.S. (2009). Effective Backbone Techniques for Ontology Integration. In: Nguyen, N.T., Szczerbicki, E. (eds) Intelligent Systems for Knowledge Management. Studies in Computational Intelligence, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04170-9_9

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  • DOI: https://doi.org/10.1007/978-3-642-04170-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04169-3

  • Online ISBN: 978-3-642-04170-9

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