Berners-Lee's compelling vision of a Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method, creating enough structured data to motivate the development of applications. However, automatic information extraction systems produce errors and are not tolerated by users, whereas user contributions incentives and management to control vandalism. We therefore propose systems that tightly integrate human and machine feedback: information extraction techniques generate candidate facts, and users correct errors, improving training data and enabling a virtuous cycle.