INTRODUCTION: CISMeF is a Quality Controlled Health Gateway using a terminology based on the Medical Subject Headings (MeSH) thesaurus that displays medical specialties (metaterms) and the relationships existing between them and MeSH terms. OBJECTIVE: The need to classify the resources within the catalogue has led us to combine this type of semantic information with domain expert knowledge for health resources categorization purposes. MATERIAL AND METHODS: A two-step categorization process consisting of mapping resource keywords to CISMeF metaterms and ranking metaterms by decreasing coverage in the resource has been developed. We evaluate this algorithm on a random set of 123 resources extracted from the CISMeF catalogue. Our gold standard for this evaluation is the manual classification provided by a domain expert, viz. a librarian of the team. RESULTS: The CISMeF algorithm shows 81% precision and 93% recall, and 62% of the resources were assigned a "fully relevant" or "fairly relevant" categorization according to strict standards. DISCUSSION: A thorough analysis of the results has enabled us to find gaps in the knowledge modeling of the CISMeF terminology. The necessary adjustments having been made, the algorithm is currently used in CISMeF for resource categorization.
INTRODUCTION: CISMeF is a Quality Controlled Health Gateway using a terminology based on the Medical Subject Headings (MeSH) thesaurus that displays medical specialties (metaterms) and the relationships existing between them and MeSH terms. OBJECTIVE: The need to classify the resources within the catalogue has led us to combine this type of semantic information with domain expert knowledge for health resources categorization purposes. MATERIAL AND METHODS: A two-step categorization process consisting of mapping resource keywords to CISMeF metaterms and ranking metaterms by decreasing coverage in the resource has been developed. We evaluate this algorithm on a random set of 123 resources extracted from the CISMeF catalogue. Our gold standard for this evaluation is the manual classification provided by a domain expert, viz. a librarian of the team. RESULTS: The CISMeF algorithm shows 81% precision and 93% recall, and 62% of the resources were assigned a "fully relevant" or "fairly relevant" categorization according to strict standards. DISCUSSION: A thorough analysis of the results has enabled us to find gaps in the knowledge modeling of the CISMeF terminology. The necessary adjustments having been made, the algorithm is currently used in CISMeF for resource categorization.