Interactions between cancer drugs and dietary supplements are clinically important and have not been extensively investigated through mining of the biomedical literature. We report on a previously introduced method now enhanced by machine learning-based filtering. Potential interactions are extracted by using relationships in the form of semantic predications. Semantic predications stored in SemMedDB, a database of structured knowledge generated from MEDLINE, were filtered and connected by two interaction pathways to explore potential drug-supplement interactions (DSIs). The lasso regression filter was trained by using SemRep output features in an expert annotated corpus and used to rank retrieved predications by predicted precision. We found not only known interactions but also inferred several unknown potential DSIs by appropriate filtering and linking of semantic predications.