This paper describes our approach to the challenge of graph-based tag recommendation in social bookmarking services. Along the ECML PKDD 2009 Discovery Challenge, we design a tag recommender that accurately predicts the tagging behavior of users within the Bibsonomy bookmarking service. We find that the tagging vocabularies among folksonomy users differ radically due to multilingual aspects as well as heterogeneous tagging habits. Our model overcomes the prediction problem resulting from these heterogeneities by translating user vocabularies, so called personomies, to the global folksonomy vocabulary and vice versa. Furthermore we combine our user-centric translation approach with item-centric methods to achieve more accurate solutions. Since our method is purely graph-based, it can also readily be applied to other folksonomies.