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Related Experiment Videos

A conceptual model for information retrieval with UMLS

M Joubert1, M Fieschi, J J Robert

  • 1CERTIM, Faculté de Médecine, Marseille, France.

Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1993
PubMed
Summary
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Conceptual graphs effectively model user queries for large databases. This approach simplifies translating natural language questions into computer-understandable search terms using the Unified Medical Language System (UMLS).

Area of Science:

  • Computer Science
  • Information Science
  • Artificial Intelligence

Background:

  • Information retrieval from large databases is complex, often requiring multiple search steps.
  • End-users struggle to translate natural language questions into formal query languages.
  • Conceptual graphs offer a powerful method for knowledge representation due to their semantic network structure.

Purpose of the Study:

  • To demonstrate the suitability of conceptual graphs for modeling end-user queries.
  • To bridge the gap between natural language questions and computer-based information retrieval systems.
  • To leverage the Unified Medical Language System (UMLS) for enhanced query understanding.

Main Methods:

  • Utilizing conceptual graphs for natural language analysis and understanding.

Related Experiment Videos

  • Applying conceptual graphs to knowledge representation tasks.
  • Modeling end-user queries based on the UMLS thesaurus and semantic network.

    • Main Results:

    • Conceptual graphs provide an expressive and suitable formalism for representing user queries.
    • The proposed method facilitates efficient parsing of user questions into query languages.
    • Integration with UMLS enhances the semantic understanding of queries.

    Conclusions:

    • Conceptual graphs are a viable tool for improving information retrieval by accurately modeling user queries.
    • This approach simplifies the interaction between end-users and complex information systems.
    • The study highlights the potential of conceptual graphs in semantic-based information access.