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A Flexible Framework To Experiment With Ontology Learning Techniques

  • Ricardo Gacitua
  • Pete Sawyer
  • Paul Rayson

Abstract

Ontology learning refers to extracting conceptual knowledge from several sources and building an ontology from scratch, enriching, or adapting an existing ontology. It uses methods from a diverse spectrum of fields such as Natural Language Processing, Artificial Intelligence and Machine learning. However, a crucial challenging issue is to quantitatively evaluate the usefulness and accuracy of both techniques and combinations of techniques, when applied to ontology learning. It is an interesting problem because there are no published comparative studies. We are developing a flexible framework for ontology learning from text which provides a cyclical process that involves the successive application of various NLP techniques and learning algorithms for concept extraction and ontology modelling. The framework provides support to evaluate the usefulness and accuracy of different techniques and possible combinations of techniques into specific processes, to deal with the above challenge. We show our framework’s efficacy as a workbench for testing and evaluating concept identification. Our initial experiment supports our assumption about the usefulness of our approach.

Keywords

Machine Learning Technique Domain Ontology Candidate Term Document Corpus Reference Ontology 
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 London Limited 2008

Authors and Affiliations

  • Ricardo Gacitua
    • 1
  • Pete Sawyer
    • 1
  • Paul Rayson
    • 1
  1. 1.Computing DepartmentLancaster UniversityUK

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