PubMed · 5134718
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P M Moodie, V Bulteau. 1971-12-04. Cohort.. https://pubmed.ncbi.nlm.nih.gov/5134718/
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The aim of the paper is to look at the medical terminology in three different languages--English, Russian and Slovak and to estimate the trends of synonym formation as well as their emphasizes role in communication. The author that the problem of synonymy is peculiar especially to clinical medicine terminology, i.e. it relates only to a small extent anatomical nomenclature. According to motivative signs of terms, synonyms are divided into 2 groups: equivalent and interpretative synonyms. In keeping with criteria determined by basic characteristics of a term in medical terminology there are often preferred international expressions. In recent years a great number of English expressions have entered into medical terminology due to the fact that English had became a language of international communication. From the practical point view undoubtedly, it is a positive tendency, because it facilitates international communication. In this respect Russian orthography is considered to be an exception, since Cyrillic alphabet often makes international communication more complicated. (Ref. 10.).
Polysemy is a bottleneck for the demanding needs of semantic data management. We suggest the importance of a well-founded conceptual analysis for understanding some systematic structures underlying polysemy in the medical lexicon. We present some cases studies, which exploit the methods (ontological integration and general theories) and tools (description logics and ontology libraries) of the ONIONS methodology defined elsewhere by the authors. This paper addresses an aspect (systematic metomymies) of the project we are involved in, which investigates the feasibility of building a large-scale ontology library of medicine that integrates the most important medical terminology banks.
Morphological knowledge is useful for medical language processing, information retrieval and terminology or ontology development. We show how a large volume of morphological associations between words can be learnt from existing medical terminologies by taking advantage of the semantic relations already encoded between terms in these terminologies: synonymy, hierarchy and transversal relations. The method proposed relies on no a priori linguistic knowledge. Since it can work with different relations between terms, it can be applied to any structured terminology. Tested on SNOMED and ICD in French and English, it proves to identify fairly reliable morphological relations (precision > 90%) with a good coverage (over 88% compared to the UMLS lexical variant generation program). For English words with a stem longer than 3 characters, recall reaches 98.8% for inflection and 94.7% for derivation.