Ontology-based error detection in SNOMED-CT.

Quality assurance in large terminologies is a difficult issue. We present two algorithms that can help terminology developers and users to identify potential areas of improvement. We demonstrate the methodology by applying the algorithms to one of the most popular terminologies, SNOMED-CT. Analysis of the results provides evidence for the thesis that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.