id: 05978202
dt: a
an: 05978202
au: Soualmia, Lina F.; Griffon, Nicolas; Grosjean, Julien; Darmoni, Stéfan J.
ti: Improving information retrieval by meta-modelling medical terminologies.
so: Peleg, Mor (ed.) et al., Artificial intelligence in medicine. 13th
    conference on artificial intelligence in medicine, AIME 2011, Bled,
    Slovenia, July 2‒6, 2011. Proceedings. Berlin: Springer (ISBN
    978-3-642-22217-7/pbk). Lecture Notes in Computer Science 6747. Lecture
    Notes in Artificial Intelligence, 215-219 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: meta-modelling; controlled vocabularies; information retrieval
ci: 
li: doi:10.1007/978-3-642-22218-4_26
ab: Summary: 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.
rv: