Date Published:
2008Abstract:
OBJECTIVES: The aim of this study is to provide to indexers MeSH terms to be considered as major ones in a list of terms automatically extracted from a document.
MATERIAL AND METHODS: We propose a method combining symbolic knowledge - the UMLS Metathesaurus and Semantic Network - and statistical knowledge drawn from co-occurrences of terms in the CISMeF database (a French-language quality-controlled health gateway) using data mining measures. The method was tested on CISMeF corpus of 293 resources.
RESULTS: There was a proportion of 0.37+/-0.26 major terms in the processed records. The method produced lists of terms with a proportion of terms initially pointed out as major of 0.54+/-0.31.
DISCUSSION: The method we propose reduces the number of terms, which seem not useful for content description of resources, such as "check tags", but retains the most descriptive ones. Discarding these terms is accounted for by: 1) the removal by using semantic knowledge of associations of concepts bearing no real medical significance, 2) the removal by using statistical knowledge of nonstatistically significant associations of terms.
CONCLUSION: This method can assist effectively indexers in their daily work and will be soon applied in the CISMeF system.
