Generating referring expressions has received considerable attention in Natural Language Generation. In recent years we start seeing deployments of referring expression generators moving away from limited domains with custom-made ontologies. In this work, we explore the feasibility of using large scale ontologies for open domain referring expression generation, an important task for summarization by re-generation. Our experiments on a fully annotated anaphora resolution training set and a larger, volunteer-submitted news corpus show that existing algorithms are efficient enough to deal with large scale ontologies but need to be extended to deal with undefined values and some measure for information salience.