Lexically-suggested hyponymic relations among medical terms and their representation in the UMLS

Objective: Among the various methods for identifying thesaurus relati ons from text corpora, methods based on head modifier relation are interesting in the context of medical terminologies, especially for those terms which differ from one another by only one modifier. Adjectival modifiers play a particular role because they usually introduce a hyponymic relation. This study focuses on comparing lexically-suggested hyponym ic relations among medical terms to inter-concept relationships represented in the Uni fied Medical Language System (UMLS) Metathesaurus. Methods: Adjectival modifiers were identified from 63,000 medical te rms from the UMLS Metathesaurus, and transformed terms were generated by removing them from the original terms. Candidate hyponymic relations were then tested against inte r-concept relationships recorded in the UMLS Metathesaurus. Results: In 50% of the cases, suggested hyponymic relations were present in the UMLS Metathesaurus. In 25% of the cases, the original term and the tra nsformed terms were “siblings” in the UMLS. In the remaining 25%, no relationship was r ecorded in the UMLS between these two terms. The lack of relationships observed in the UMLS Metathesaurus is disc ussed. Additional methods for automatically assessing the suggested hyponymic relat ions are proposed. Further research directions are briefly presented.

[1]  Olivier Bodenreider,et al.  Beyond synonymy: exploiting the UMLS semantics in mapping vocabularies , 1998, AMIA.

[2]  Olivier Bodenreider,et al.  Case Report: Evaluation of the Unified Medical Language System as a Medical Knowledge Source , 1998, J. Am. Medical Informatics Assoc..

[3]  D. Lindberg,et al.  Unified Medical Language System , 2020, Definitions.

[4]  Gerda Ruge,et al.  Automatic Detection of Thesaurus relations for Information Retrieval Applications , 1997, Foundations of Computer Science: Potential - Theory - Cognition.

[5]  Penelope Sibun,et al.  A Practical Part-of-Speech Tagger , 1992, ANLP.

[6]  William T. Hole,et al.  Discovering missed synonymy in a large concept-oriented Metathesaurus , 2000, AMIA.

[7]  Wilfried Brauer,et al.  Foundations of computer science : potential--theory--cognition , 1997 .

[8]  R. Côté Systematized nomenclature of human and veterinary medicine : SNOMED international , 1993 .

[9]  Marti A. Hearst Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.

[10]  James J. Cimino,et al.  Research Paper: Auditing the Unified Medical Language System with Semantic Methods , 1998, J. Am. Medical Informatics Assoc..

[11]  Oliviero Stock,et al.  Proceedings of the third conference on Applied natural language processing , 1992 .

[12]  D. Lindberg,et al.  The Unified Medical Language System , 1993, Methods of Information in Medicine.

[13]  Lawrence Hunter,et al.  Extracting Molecular Binding Relationships from Biomedical Text , 2000, ANLP.