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Retrieving definitional content for ontology development.

L Smith1, W J Wilbur

  • 1National Center for Biotechnology Information, NIH, NLM, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. lsmith@ncbi.nlm.nih.gov

Computational Biology and Chemistry
|November 24, 2004
PubMed
Summary
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This study introduces an automated method to identify definitional content within expert writing using machine learning. The system predicts the likelihood that sentences contain information relevant to concept definitions.

Area of Science:

  • Knowledge Representation and Ontologies
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Ontology construction relies on understanding concept meaning and usage.
  • Traditional definitions from dictionaries may not capture nuanced expert usage.
  • Expert writing offers a rich source for concept clarification.

Purpose of the Study:

  • To develop an automated procedure for extracting definitional content from expert texts.
  • To leverage machine learning for identifying sentences containing definition-relevant information.
  • To enhance ontology construction by utilizing real-world expert language.

Main Methods:

  • Utilized machine learning algorithms trained on phrasal features.
  • Trained models to assess sentence similarity to existing glossary definitions within the same text.

Related Experiment Videos

  • Developed a system to output a probability score for definitional content in each sentence.

    • Main Results:

    • The automated procedure successfully identifies sentences with definitional content.
    • The system provides a probabilistic measure of relevance, not a concise definition.
    • Evaluation demonstrated effectiveness for terms with and without explicit definitions.

    Conclusions:

    • Automated extraction of definitional content from expert writing is feasible.
    • This approach can supplement traditional definition sources for ontology development.
    • Machine learning offers a powerful tool for analyzing linguistic usage in specialized domains.