Ontology augmentation: combining semantic web and text resources

  • Authors:
  • Miriam Fernandez;Ziqi Zhang;Vanesa Lopez;Victoria Uren;Enrico Motta

  • Affiliations:
  • Open University, Milton Keynes, United Kingdom;University of Sheffield, Sheffield, United Kingdom;Open University, Milton Keynes, United Kingdom;University of Sheffield, Sheffield, United Kingdom;Open University, Milton Keynes, United Kingdom

  • Venue:
  • Proceedings of the sixth international conference on Knowledge capture
  • Year:
  • 2011

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Abstract

This work investigates the process of selecting, extracting and reorganizing content from Semantic Web information sources, to produce an ontology meeting the specifications of a particular domain and/or task. The process is combined with traditional text-based ontology learning methods to achieve tolerance to knowledge incompleteness. The paper describes the approach and presents experiments in which an ontology was built for a diet evaluation task. Although the example presented concerns the specific case of building a nutritional ontology, the methods employed are domain independent and transferrable to other use cases.