This work explores the use of semantic information from background knowledge sources for the task of relation mining between medical entities such as diseases, drugs, and their functional effects/actions. We hypothesize that the semantics of medical entities, and the information about them in different knowledge sources play an important role in determining their interactions and can thus be exploited to infer relations between these entities. We capture entities' semantics using a number of resources such as Wikipedia, UMLS Semantic Network, MEDCIN, MeSH and SNOMED. De-pending on coverage and specificity of the resources, and features of interest, different classifiers are learnt. An ensemble based approach is then used to fuse together individual predictions. Using a human-curated ontology as the gold standard, the proposed approach has been used to recognize ten medical relations of interest. We show that the proposed approach achieves substantial improvements in both coverage and performance over a distant supervision based baseline that uses sentence-level information. Finally, we also show that even a simple ensemble approach that combines all the semantic information is able to get the best coverage and performance.