Supporting biomedical ontology evolution by identifying outdated concepts and the required type of change.

The consistent evolution of ontologies is a major challenge for systems using semantically enriched data, for example, for annotating, indexing, or reasoning. The biomedical domain is a typical example where ontologies, expressed with different formalisms, have been used for a long time and whose dynamic nature requires the regular revision of underlying systems. However, the automatic identification of outdated concepts and proposition of revision actions to update them are still open research questions. Solutions to these problems are of great interest to organizations that manage huge and dynamic ontologies. In this paper, we present an approach for (i) identifying the concepts of an ontology that require revision and (ii) suggesting the type of revision. Our analysis is based on three aspects: structural information encoded in the ontology, relational information gained from external source of knowledge (i.e., PubMed and UMLS) and temporal information derived from the history of the ontology. Our approach aims to evaluate different methods and parameters used by supervised learning classifiers to identify both the set of concepts that need revision, and the type of revision. We applied our approach to four well-known biomedical ontologies/terminologies (ICD-9-CM, MeSH, NCIt and SNOMED CT) and compared our results to similar approaches. Our model shows accuracy ranging from 68% (for SNOMED CT) to 91% (for MeSH), and an average of 71% when considering all datasets together.