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Semi-supervised Learning of Action Ontology from Domain-Specific Corpora

  • Irena Markievicz
  • Daiva Vitkute-Adzgauskiene
  • Minija Tamosiunaite
Part of the Communications in Computer and Information Science book series (CCIS, volume 403)

Abstract

The paper presents research results, showing how unsupervised and supervised ontology learning methods can be combined in an action ontology building approach. A framework for action ontology building from domainspecific corpus texts is suggested, using different natural language processing techniques, such as collocation extraction, frequency lists, word space model, etc. The suggested framework employs additional knowledge sources of WordNet and VerbNet with structured linguistic and semantic information. Results from experiments with crawled chemical laboratory corpus texts are given.

Keywords

action ontology semi-supervised ontology learning natural language processing corpus linguistics domain-specific corpus 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Irena Markievicz
    • 1
  • Daiva Vitkute-Adzgauskiene
    • 1
  • Minija Tamosiunaite
    • 2
  1. 1.Faculty of InformaticsVytautas Magnus UniversityKaunasLithuania
  2. 2.Bernstein Center for Computational NeuroscienceUniversity of GottingenLithuania

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