Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with the problem, we integrate core ontologies as background knowledge into the process of clustering text documents. Our experimental evaluations compare clustering techniques based on pre-categorizations of texts from Reuters newsfeeds and on a smaller domain of an eLearning course about Java. In the experiments, improvements of results by background knowledge compared to a baseline without background knowledge can be shown in many interesting combinations.