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An Unsupervised Method for Ontology Population from the Web

  • Hilário Tomaz
  • Rinaldo Lima
  • João Emanoel
  • Fred Freitas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7637)

Abstract

Knowledge engineers have had difficulty in automatically constructing and populating domain ontologies, mainly due to the well-known knowledge acquisition bottleneck. In this paper, we attempt to alleviate this problem by proposing an iterative unsupervised approach to identifying and extracting ontological class instances from the Web. The proposed approach considers the Web as a big corpus and relies on a confidence-weighted metric based on semantic measures and web-scale statistics as types of evidence. Moreover, our iterative method is able to learn, to some extent, domain-specific linguistic patterns for extracting ontological class instances. We obtained encouraging results for the final ranking of candidate instances as well as an accuracy performance up to 97% for the patterns found by our method.

Keywords

ontology population ontology-based information extraction confidence metric pattern learning similarity measure 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hilário Tomaz
    • 1
  • Rinaldo Lima
    • 1
  • João Emanoel
    • 1
  • Fred Freitas
    • 1
  1. 1.Informatics CenterFederal University of PernambucoRecifeBrazil

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