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Journal of Healthcare Engineering
Volume 2017, Article ID 3495723, 11 pages
https://doi.org/10.1155/2017/3495723
Research Article

Taxonomy-Based Approaches to Quality Assurance of Ontologies

1Informatics Department, New Jersey Institute of Technology, Newark, NJ 07102-1982, USA
2Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102-1982, USA

Correspondence should be addressed to Michael Halper; ude.tijn@replah.leahcim

Received 7 May 2017; Accepted 6 August 2017; Published 11 October 2017

Academic Editor: Jiang Bian

Copyright © 2017 Michael Halper et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ontologies are important components of health information management systems. As such, the quality of their content is of paramount importance. It has been proven to be practical to develop quality assurance (QA) methodologies based on automated identification of sets of concepts expected to have higher likelihood of errors. Four kinds of such sets (called QA-sets) organized around the themes of complex and uncommonly modeled concepts are introduced. A survey of different methodologies based on these QA-sets and the results of applying them to various ontologies are presented. Overall, following these approaches leads to higher QA yields and better utilization of QA personnel. The formulation of additional QA-set methodologies will further enhance the suite of available ontology QA tools.