Despite unified data models, such as the Resource Description Framework (Rdf) on structural level and the corresponding query language Sparql, the integration and usage of Linked Open Data faces major heterogeneity challenges on the semantic level. Incorrect use of ontology concepts and class properties impede the goal of machine readability and knowledge discovery. For example, users searching for movies with a certain artist cannot rely on a single given property artist, because some movies may be connected to that artist by the predicate starring. In addition, the information need of a data consumer may not always be clear and her interpretation of given schemata may differ from the intentions of the ontology engineer or data publisher. It is thus necessary to either support users during query formulation or to incorporate implicitly related facts through predicate expansion. To this end, we introduce a data-driven synonym discovery algorithm for predicate expansion. We applied our algorithm to various data sets as shown in a thorough evaluation of different strategies and rule-based techniques for this purpose.