The focus of contemporary Web information retrieval systems has been to provide efficient support for the querying and retrieval of relevant documents. More recently, information retrieval over semantic metadata extracted from the Web has received an increasing amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this metadata is an interesting and challenging research topic. Just as the ranking of documents is a critical component of todays search engines, the ranking of complex relationships will be an important component in tomorrows Semantic Web analytics engines. Building upon our recent work on specifying and discovering complex relationships in RDF (Resource Description Framework) data, called Semantic Associations, we present a flexible ranking approach which can be used to identify more interesting and relevant relationships on the Semantic Web. Additionally, we demonstrate our ranking schemes effectiveness through an empirical evaluation over a real-world dataset.