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NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce

A structural disparity of the subsumption relationship between FMA and SNOMED CT’s Body Structure sub-hierarchy is that while the is-a relation in FMA has a tree structure, the corresponding relation in Body Structure is not even a lattice. This paper introduces a method called NEO, for non-lattice...

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Autores principales: Zhu, Wei, Zhang, Guo-Qiang, Tao, Shiqiang, Sun, Mengmeng, Cui, Licong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525277/
https://www.ncbi.nlm.nih.gov/pubmed/26306275
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author Zhu, Wei
Zhang, Guo-Qiang
Tao, Shiqiang
Sun, Mengmeng
Cui, Licong
author_facet Zhu, Wei
Zhang, Guo-Qiang
Tao, Shiqiang
Sun, Mengmeng
Cui, Licong
author_sort Zhu, Wei
collection PubMed
description A structural disparity of the subsumption relationship between FMA and SNOMED CT’s Body Structure sub-hierarchy is that while the is-a relation in FMA has a tree structure, the corresponding relation in Body Structure is not even a lattice. This paper introduces a method called NEO, for non-lattice embedding of FMA fragments into the Body Structure sub-hierarchy to understand (1) this structural disparity, and (2) its potential utility in analyzing non-lattice fragments in SNOMED CT. NEO consists of four steps. First, transitive, upper- and down-closures are computed for FMA and SNOMED CT using MapReduce, a modern scalable distributed computing technique. Secondly, UMLS mappings between FMA and SNOMED CT concepts are used to identify equivalent concepts in non-lattice fragments from Body Structure. Then, non-lattice fragments in the Body Structure sub-hierarchy are extracted, and FMA concepts matching those in the non-lattice fragments are used as the seeds to generate the corresponding FMA fragments. Lastly, the corresponding FMA fragments are embedded to the non-lattice fragments for comparative visualization and analysis. After identifying 8,428 equivalent concepts between the collection of over 30,000 concepts in Body Structure and the collection of over 83,000 concepts in FMA using UMLS equivalent concept mappings, 2,117 shared is-a relations and 5,715 mismatched relations were found. Among Body Structure’s 90,465 non-lattice fragments, 65,968 (73%) contained one or more is-a relations that are in SNOMED CT but not in FMA, even though they have equivalent source and target concepts. This shows that SNOMED CT may be more liberal in classifying a relation as is-a, a potential explanation for the fragments not conforming to the lattice property.
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spelling pubmed-45252772015-08-24 NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce Zhu, Wei Zhang, Guo-Qiang Tao, Shiqiang Sun, Mengmeng Cui, Licong AMIA Jt Summits Transl Sci Proc Articles A structural disparity of the subsumption relationship between FMA and SNOMED CT’s Body Structure sub-hierarchy is that while the is-a relation in FMA has a tree structure, the corresponding relation in Body Structure is not even a lattice. This paper introduces a method called NEO, for non-lattice embedding of FMA fragments into the Body Structure sub-hierarchy to understand (1) this structural disparity, and (2) its potential utility in analyzing non-lattice fragments in SNOMED CT. NEO consists of four steps. First, transitive, upper- and down-closures are computed for FMA and SNOMED CT using MapReduce, a modern scalable distributed computing technique. Secondly, UMLS mappings between FMA and SNOMED CT concepts are used to identify equivalent concepts in non-lattice fragments from Body Structure. Then, non-lattice fragments in the Body Structure sub-hierarchy are extracted, and FMA concepts matching those in the non-lattice fragments are used as the seeds to generate the corresponding FMA fragments. Lastly, the corresponding FMA fragments are embedded to the non-lattice fragments for comparative visualization and analysis. After identifying 8,428 equivalent concepts between the collection of over 30,000 concepts in Body Structure and the collection of over 83,000 concepts in FMA using UMLS equivalent concept mappings, 2,117 shared is-a relations and 5,715 mismatched relations were found. Among Body Structure’s 90,465 non-lattice fragments, 65,968 (73%) contained one or more is-a relations that are in SNOMED CT but not in FMA, even though they have equivalent source and target concepts. This shows that SNOMED CT may be more liberal in classifying a relation as is-a, a potential explanation for the fragments not conforming to the lattice property. American Medical Informatics Association 2015-03-25 /pmc/articles/PMC4525277/ /pubmed/26306275 Text en ©2015 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Zhu, Wei
Zhang, Guo-Qiang
Tao, Shiqiang
Sun, Mengmeng
Cui, Licong
NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce
title NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce
title_full NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce
title_fullStr NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce
title_full_unstemmed NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce
title_short NEO: Systematic Non-Lattice Embedding of Ontologies for Comparing the Subsumption Relationship in SNOMED CT and in FMA Using MapReduce
title_sort neo: systematic non-lattice embedding of ontologies for comparing the subsumption relationship in snomed ct and in fma using mapreduce
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525277/
https://www.ncbi.nlm.nih.gov/pubmed/26306275
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