Analysis of readability and structural accuracy in SNOMED CT
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RESEARCH
Analysis of readability and structural accuracy in SNOMED CT Francisco Abad‑Navarro1,2 , Manuel Quesada‑Martínez3 , Astrid Duque‑Ramos4 and Jesualdo Tomás Fernández‑Breis1,2*
Abstract Background: The increasing adoption of ontologies in biomedical research and the growing number of ontologies available have made it necessary to assure the quality of these resources. Most of the well-established ontologies, such as the Gene Ontology or SNOMED CT, have their own quality assurance processes. These have demonstrated their usefulness for the maintenance of the resources but are unable to detect all of the modelling flaws in the ontolo‑ gies. Consequently, the development of efficient and effective quality assurance methods is needed. Methods: Here, we propose a series of quantitative metrics based on the processing of the lexical regularities exist‑ ing in the content of the ontology, to analyse readability and structural accuracy. The readability metrics account for the ratio of labels, descriptions, and synonyms associated with the ontology entities. The structural accuracy metrics evaluate how two ontology modelling best practices are followed: (1) lexically suggest locally define (LSLD), that is, if what is expressed in natural language for humans is available as logical axioms for machines; and (2) systematic nam‑ ing, which accounts for the amount of label content of the classes in a given taxonomy shared. Results: We applied the metrics to different versions of SNOMED CT. Both readability and structural accuracy metrics remained stable in time but could capture some changes in the modelling decisions in SNOMED CT. The value of the LSLD metric increased from 0.27 to 0.31, and the value of the systematic naming metric was around 0.17. We analysed the readability and structural accuracy in the SNOMED CT July 2019 release. The results showed that the fulfilment of the structural accuracy criteria varied among the SNOMED CT hierarchies. The value of the metrics for the hierarchies was in the range of 0–0.92 (LSLD) and 0.08–1 (systematic naming). We also identified the cases that did not meet the best practices. Conclusions: We generated useful information about the engineering of the ontology, making the following contri‑ butions: (1) a set of readability metrics, (2) the use of lexical regularities to define structural accuracy metrics, and (3) the generation of quality assurance information for SNOMED CT. Keywords: Ontologies, Quality assurance, Quality metrics, Readability, Structural accuracy Background Recently, ontologies and terminologies, such as the SNOMED CT [1], the Gene Ontology (GO) [2, 3], or the Disease Ontology (DO) [4], have demonstrated their *Correspondence: [email protected] 2 Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain Full list of author information is available at the end of the article
usefulness for supporting biomedical research, thus making their quality and maintenance
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