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Biomedical terminologies and ontologies are frequently described as enabling resources in text mining systems [e.g., 1, 2, 3]. These resources are used to supports tasks such as entity recognition (i.e., the identification of biomedical

Abstract. This paper proposes a methodology to achieve the automatic inheritance of SNOMED CT relations applied to MeSH preferred terms using UMLS as knowledge source server. We propose an interoperability wildcard to achieve this objective. A quantitative and a qualitative analysis were performed on top four SNOMED CT relations inherited between MeSH preferred terms. A total of 12,030 couples of MeSH preferred terms are in relation via at least one SNOMED CT relationship. For the top-four relations inherited between MeSH preferred terms, overall 79.25 % of them are relevant, 16.25 % as intermediate and 4.5 % as irrelevant, as judged by a medical librarian. This work should lead to an optimization of multi-terminology indexing tools, multi-terminology information retrieval and navigation among a multi-terminology server. Keywords. algorithm, terminology, SNOMED CT, Medical Subject Headings, relationships 1.

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