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An Abstraction-Based Data Model for Information Retrieval

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AI 2009: Advances in Artificial Intelligence (AI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5866))

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Abstract

Language ontologies provide an avenue for automated lexical analysis that may be used to supplement existing information retrieval methods. This paper presents a method of information retrieval that takes advantage of WordNet, a lexical database, to generate paths of abstraction, and uses them as the basis for an inverted index structure to be used in the retrieval of documents from an indexed corpus. We present this method as a entree to a line of research on using ontologies to perform word-sense disambiguation and improve the precision of existing information retrieval techniques.

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Author information

Authors and Affiliations

  1. Montana State University Department of Computer Science, Bozeman, MT, 59717-3880

    Richard A. McAllister & Rafal A. Angryk

Authors
  1. Richard A. McAllister
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  2. Rafal A. Angryk
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Editor information

Editors and Affiliations

  1. Clayton School of Information Technology, Monash University, 3800, Clayton, VIC, Australia

    Ann Nicholson

  2. School of Computer Science and Information Technology, RMIT University, 3001, Melbourne, VIC, Australia

    Xiaodong Li

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© 2009 Springer-Verlag Berlin Heidelberg

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McAllister, R.A., Angryk, R.A. (2009). An Abstraction-Based Data Model for Information Retrieval. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_57

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  • DOI: https://doi.org/10.1007/978-3-642-10439-8_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10438-1

  • Online ISBN: 978-3-642-10439-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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