Abstract

OWL Ontologies are already being used in many application domains. In particular, OWL is extensively used in the clinical sciences; prominent examples of OWL ontologies are the National Cancer Institute (NCI) Thesaurus, SNOMED CT, the Gene Ontology (GO), the Foundational Model of Anatomy (FMA), and GALEN. These ontologies are large and complex; for example, SNOMED currently describes more than 350.000 concepts whereas NCI and GALEN describe around 50.000 concepts. Furthermore, these ontologies are in continuous evolution; for example the developers of NCI and GO perform approximately 350 additions of new entities and 25 deletions of obsolete entities each month [1]. Most realistic ontologies, including the ones just mentioned, are being developed collaboratively. The developers of an ontology can be geographically distributed and may contribute in different ways and to different extents. Maintaining such large ontologies in a collaborative way is a highly complex process, which involves tracking and managing the frequent changes to the ontology, reconciling conflicting views of the domain from different developers, minimising the introduction of errors (e.g., ensuring

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