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A chemical specialty semantic network for the Unified Medical Language System

BACKGROUND: Terms representing chemical concepts found the Unified Medical Language System (UMLS) are used to derive an expanded semantic network with mutually exclusive semantic types. The UMLS Semantic Network (SN) is composed of a collection of broad categories called semantic types (STs) that ar...

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Autores principales: Morrey, C Paul, Perl, Yehoshua, Halper, Michael, Chen, Ling, Gu, Huanying “Helen”
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3428652/
https://www.ncbi.nlm.nih.gov/pubmed/22577759
http://dx.doi.org/10.1186/1758-2946-4-9
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author Morrey, C Paul
Perl, Yehoshua
Halper, Michael
Chen, Ling
Gu, Huanying “Helen”
author_facet Morrey, C Paul
Perl, Yehoshua
Halper, Michael
Chen, Ling
Gu, Huanying “Helen”
author_sort Morrey, C Paul
collection PubMed
description BACKGROUND: Terms representing chemical concepts found the Unified Medical Language System (UMLS) are used to derive an expanded semantic network with mutually exclusive semantic types. The UMLS Semantic Network (SN) is composed of a collection of broad categories called semantic types (STs) that are assigned to concepts. Within the UMLS’s coverage of the chemical domain, we find a great deal of concepts being assigned more than one ST. This leads to the situation where the extent of a given ST may contain concepts elaborating variegated semantics. A methodology for expanding the chemical subhierarchy of the SN into a finer-grained categorization of mutually exclusive types with semantically uniform extents is presented. We call this network a Chemical Specialty Semantic Network (CSSN). A CSSN is derived automatically from the existing chemical STs and their assignments. The methodology incorporates a threshold value governing the minimum size of a type’s extent needed for inclusion in the CSSN. Thus, different CSSNs can be created by choosing different threshold values based on varying requirements. RESULTS: A complete CSSN is derived using a threshold value of 300 and having 68 STs. It is used effectively to provide high-level categorizations for a random sample of compounds from the “Chemical Entities of Biological Interest” (ChEBI) ontology. The effect on the size of the CSSN using various threshold parameter values between one and 500 is shown. CONCLUSIONS: The methodology has several potential applications, including its use to derive a pre-coordinated guide for ST assignments to new UMLS chemical concepts, as a tool for auditing existing concepts, inter-terminology mapping, and to serve as an upper-level network for ChEBI.
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spelling pubmed-34286522012-08-29 A chemical specialty semantic network for the Unified Medical Language System Morrey, C Paul Perl, Yehoshua Halper, Michael Chen, Ling Gu, Huanying “Helen” J Cheminform Research Article BACKGROUND: Terms representing chemical concepts found the Unified Medical Language System (UMLS) are used to derive an expanded semantic network with mutually exclusive semantic types. The UMLS Semantic Network (SN) is composed of a collection of broad categories called semantic types (STs) that are assigned to concepts. Within the UMLS’s coverage of the chemical domain, we find a great deal of concepts being assigned more than one ST. This leads to the situation where the extent of a given ST may contain concepts elaborating variegated semantics. A methodology for expanding the chemical subhierarchy of the SN into a finer-grained categorization of mutually exclusive types with semantically uniform extents is presented. We call this network a Chemical Specialty Semantic Network (CSSN). A CSSN is derived automatically from the existing chemical STs and their assignments. The methodology incorporates a threshold value governing the minimum size of a type’s extent needed for inclusion in the CSSN. Thus, different CSSNs can be created by choosing different threshold values based on varying requirements. RESULTS: A complete CSSN is derived using a threshold value of 300 and having 68 STs. It is used effectively to provide high-level categorizations for a random sample of compounds from the “Chemical Entities of Biological Interest” (ChEBI) ontology. The effect on the size of the CSSN using various threshold parameter values between one and 500 is shown. CONCLUSIONS: The methodology has several potential applications, including its use to derive a pre-coordinated guide for ST assignments to new UMLS chemical concepts, as a tool for auditing existing concepts, inter-terminology mapping, and to serve as an upper-level network for ChEBI. BioMed Central 2012-05-11 /pmc/articles/PMC3428652/ /pubmed/22577759 http://dx.doi.org/10.1186/1758-2946-4-9 Text en Copyright ©2012 Morrey et al.; licensee Chemistry Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Morrey, C Paul
Perl, Yehoshua
Halper, Michael
Chen, Ling
Gu, Huanying “Helen”
A chemical specialty semantic network for the Unified Medical Language System
title A chemical specialty semantic network for the Unified Medical Language System
title_full A chemical specialty semantic network for the Unified Medical Language System
title_fullStr A chemical specialty semantic network for the Unified Medical Language System
title_full_unstemmed A chemical specialty semantic network for the Unified Medical Language System
title_short A chemical specialty semantic network for the Unified Medical Language System
title_sort chemical specialty semantic network for the unified medical language system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3428652/
https://www.ncbi.nlm.nih.gov/pubmed/22577759
http://dx.doi.org/10.1186/1758-2946-4-9
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