Reducing Uncertainty of Schema Matching via

Schema matching is a central challenge for data integration sys-tems. Automated tools are often uncertain about schema matchings they suggest, and this uncertainty is inherent since it arises from the inability of the schema to fully capture the semantics of the repre-sented data. Human common sense can often help. Inspired by the popularity and the success of easily accessible crowdsourcing platforms, we explore the use of crowdsourcing to reduce the un-certainty of schema matching. Since it is typical to ask simple questions on crowdsourcing plat-forms, we assume that each question, namely Correspondence Cor-rectness Question (CCQ), is to ask the crowd to decide whether a given correspondence should exist in the correct matching. We pro-pose frameworks and efficient algorithms to dynamically manage the CCQs, in order to maximize the uncertainty reduction within a limited budget of questions. We develop two novel approaches, namely “Single CCQ ” and “Multiple CCQ”, which adaptively se-lect, publish and manage the questions. We verified the value of our solutions with simulation and real implementation. 1