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Lexically Evaluating Ontology Triples Generated Automatically from Texts

  • Peter Spyns
  • Marie-Laure Reinberger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3532)

Abstract

Our purpose is to present a method to lexically evaluate the results of extracting in an unsupervised way material from text corpora to build ontologies. We have worked on a legal corpus (EU VAT directive) consisting of 43K words. The unsupervised text miner has produced a set of triples. These are to be used as preprocessed material for the construction of ontologies from scratch. A quantitative scoring method (coverage, accuracy, recall and precision metrics resulting in a 38.68%, 52.1%, 9.84% and 75.81% scores respectively) has been defined and applied.

Keywords

Frequency Class Characteristic Word Grammatical Relation Ontology Learn Relevant Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Peter Spyns
    • 1
  • Marie-Laure Reinberger
    • 2
  1. 1.STAR LabVrije Universiteit BrusselBrusselBelgium
  2. 2.CNTSUniversity of AntwerpWilrijkBelgium

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