The ontologies developed in enterprises often store sensitive information on products, processes, and the overall business. Companies are (understandably) reluctant to share them with persons outside their organization. For some use-cases, though, it is not necessarily the data but the structure that is of interest.

This paper presents “OntoAnon”, an anonymizer for ontologies. The Python-based application allows the removal of sensitive information like class-and property names, as well as annotation contents, while preserving the ontology structure and used formalisms. This allows the developing knowledge engineers to use tools like OOPS and NEOntometrics without compromising sensitive data. Further, it allows researchers to collect insensitive but valuable data on enterprise ontologies, e.g., to study evolutional processes.