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Web 2.0 & Semantic Web pp 163–175Cite as

Ontology-Based Data Mining in Digital Libraries

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Part of the book series: Annals of Information Systems ((AOIS,volume 6))

Abstract

The paper proposes matching short forms (abbreviated titles from the citation report) with their corresponding longer ones (journal titles in the digital library). The main problem is that there are often a number of syntactically different abbreviated forms for one abbreviated title in the citation report. We use character- and token-based similarity metrics to identify duplicate records. Also, we improve the process of identifying syntactically different data with the automated discovery of ontological knowledge representations such as thesauri from correctly matched data.

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Notes

  1. 1.

    Febrl, Freely Extensible Biomedical Record Linkage, http://sourceforge.net/projects/febrl

  2. 2.

    http://www.thomsonscientific.com

  3. 3.

    The author is the first author.

  4. 4.

    http://liinwww.ira.uka.de/bibliography/index

  5. 5.

    http://citeseer.ist.psu.edu

  6. 6.

    Note: titles are grouped in clusters.

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Author information

Authors and Affiliations

  1. Faculty of Security Studies, University of Belgrade, Gospodara Vučića 50, 11040, Beograd, Serbia

    Ana Kovačević

Authors
  1. Ana Kovačević
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Correspondence to Ana Kovačević .

Editor information

Editors and Affiliations

  1. School of Business Administration, University of Belgrade, Jove Ilica 154, Belgrade, 11000, Serbia

    Vladan Devedžić

  2. School of Computing &, Athabasca University, University Drive 1, Athabasca, T9S 3A3, Canada

    Dragan Gaševic

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Kovačević, A. (2010). Ontology-Based Data Mining in Digital Libraries. In: Devedžić, V., Gaševic, D. (eds) Web 2.0 & Semantic Web. Annals of Information Systems, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1219-0_7

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  • DOI: https://doi.org/10.1007/978-1-4419-1219-0_7

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  • Print ISBN: 978-1-4419-1218-3

  • Online ISBN: 978-1-4419-1219-0

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