Literature DB >> 24076436

Understanding semantic mapping evolution by observing changes in biomedical ontologies.

Julio Cesar dos Reis1, Cédric Pruski2, Marcos Da Silveira3, Chantal Reynaud-Delaître4.   

Abstract

Knowledge Organization Systems (KOSs) are extensively used in the biomedical domain to support information sharing between software applications. KOSs are proposed covering different, but overlapping subjects, and mappings indicate the semantic relation between concepts from two KOSs. Over time, KOSs change as do the mappings between them. This can result from a new discovery or a revision of existing knowledge which includes corrections of concepts or mappings. Indeed, changes affecting KOS entities may force the underline mappings to be updated in order to ensure their reliability over time. To tackle this open research problem, we study how mappings are affected by KOS evolution. This article presents a detailed descriptive analysis of the impact that changes in KOS have on mappings. As a case study, we use the official mappings established between SNOMED CT and ICD-9-CM from 2009 to 2011. Results highlight factors according to which KOS changes in varying degrees influence the evolution of mappings.
Copyright © 2013 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  KOS evolution; Mapping adaptation; Mapping evolution; Mapping maintenance; Ontology evolution; Semantic mappings

Mesh:

Year:  2013        PMID: 24076436     DOI: 10.1016/j.jbi.2013.09.006

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  3 in total

Review 1.  Management of Dynamic Biomedical Terminologies: Current Status and Future Challenges.

Authors:  M Da Silveira; J C Dos Reis; C Pruski
Journal:  Yearb Med Inform       Date:  2015-08-13

Review 2.  Evolution of biomedical ontologies and mappings: Overview of recent approaches.

Authors:  Anika Groß; Cédric Pruski; Erhard Rahm
Journal:  Comput Struct Biotechnol J       Date:  2016-08-26       Impact factor: 7.271

3.  Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.

Authors:  Houcemeddine Turki; Dariusz Jemielniak; Mohamed A Hadj Taieb; Jose E Labra Gayo; Mohamed Ben Aouicha; Mus'ab Banat; Thomas Shafee; Eric Prud'hommeaux; Tiago Lubiana; Diptanshu Das; Daniel Mietchen
Journal:  PeerJ Comput Sci       Date:  2022-09-29
  3 in total

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