The exploitation of sensible data associated to individuals requires a proper anonymization in order to preserve the privacy. Even though several masking methods have been designed for numerical data, very few of them deal with textual information. During the masking process, information loss should be minimized in order to enable a proper analysis of data with data mining methods. In the case of textual data, the quality of the anonymized dataset is closely related to the preservation of semantics, a dimension which has been only shallowly considered in some previous works, by using small and ad-hoc hierarchies of words. In this work we want to study the use of large and standard ontologies as the base to perform the anonymization of textual variables. We will evaluate the role of ontologies in preserving the utility of the anonymized information when a partition of the objects is done with unsupervised clustering methods. Results show that by exploiting detailed ontologies, one is able to improve the preservation of the data semantics in comparison to approaches based on ad-hoc structures and data distribution metrics.