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Potential drug-drug interactions (PDDIs) are a significant public health concern. Unfortunately, the fragmented, incomplete, and dynamic nature of evidence on PDDIs makes designing effect clinical decisions support tools very challenging. In this talk, I present a conceptual model of how evidence issues affect patient safety with respect to PDDIs. I then propose a new paradigm for representing PDDI knowledge that I hypothesize will result in more clinically useful evidence than is currently possible. Finally, I place several of my recent research projects in the context of the new paradigm and make some final suggestions for future work. Throughout the talk I try to highlight the various roles that natural language processing, Semantic Web technologies, and pharmacoepidemiology have to play in improving medication safety for patients exposed to PDDIs.