An empirically derived taxonomy of errors in SNOMED CT

Ontologies underpin methods throughout biomedicine and biomedical informatics. However, as ontologies increase in size and complexity, so does the likelihood that they contain errors. Effective methods that identify errors are typically manual and expert-driven; however, automated methods are essential for the size of modern biomedical ontologies. The effect of ontology errors on their application is unclear, creating a challenge in differentiating salient, relevant errors with those that have no discernable effect. As a first step in understanding the challenge of identifying salient, common errors at a large scale, we asked 5 experts to verify a random subset of complex relations in the SNOMED CT CORE Problem List Subset. The experts found 39 errors that followed several common patterns. Initially, the experts disagreed about errors almost entirely, indicating that ontology verification is very difficult and requires many eyes on the task. It is clear that additional empirically-based, application-focused ontology verification method development is necessary. Toward that end, we developed a taxonomy that can serve as a checklist to consult during ontology quality assurance.