  Public knowledge graphs such as DBpedia and Wikidata have been recognized as
interesting sources of background knowledge to build content-based recommender
systems. They can be used to add information about the items to be recommended
and links between those. While quite a few approaches for exploiting knowledge
graphs have been proposed, most of them aim at optimizing the recommendation
strategy while using a fixed knowledge graph. In this paper, we take a
different approach, i.e., we fix the recommendation strategy and observe
changes when using different underlying knowledge graphs. Particularly, we use
different language editions of DBpedia. We show that the usage of different
knowledge graphs does not only lead to differently biased recommender systems,
but also to recommender systems that differ in performance for particular
fields of recommendations.
