Improving information retrieval by meta-modelling medical terminologies

Authors:
Lina F. Soualmia;Nicolas Griffon;Julien Grosjean;Stéfan J. Darmoni
Affiliations:
LIM&Bio, University of Paris 13, Bobigny, France and TIBS, LITIS & CISMeF, Rouen University, Rouen, France;TIBS, LITIS & CISMeF, Rouen University, Rouen, France;TIBS, LITIS & CISMeF, Rouen University, Rouen, France;TIBS, LITIS & CISMeF, Rouen University, Rouen, France
Venue:
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Year:
2011

Citing 2
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Web Search: Public Searching of the Web (Information Science and Knowledge Management)

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Multiple Terminologies in a Health Portal: Automatic Indexing and Information Retrieval

AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine

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Abstract

This work aims at improving information retrieval in a health gateway by meta-modelling multiple terminologies related to medicine. The meta-model is based on meta-terms that gather several terms semantically related. Meta-terms, initially modelled for the MeSH thesaurus, are extended for other terminologies such as IC10 or SNOMED Int. The usefulness of this model and the relevance of information retrieval is evaluated and compared in the case of one and multiple terminologies. The results show that exploiting multiple terminologies contributes to increase recall but lowers precision.