Knowledge bases (KB) provide support for real-world decision making by exposing data in a structured format. However, constructing knowledge bases requires gathering data from many heterogeneous sources. Manual efforts for this task are accurate, but lack scalability, and automated approaches provide good coverage, but are not reliable enough for real world decision makers to trust. These two approaches to KB construction have complementary strengths: in this paper we propose a novel framework for supporting human proposed edits to knowledge bases.