Schema matching is a basic operation in the data integration process, and several tools for automating it have been proposed and evaluated in the database community. While in many domains these tools succeed to find the right matching between concepts, empirical analysis shows that there is no single algorithm that is guaranteed to succeed in all possible domains. In this paper we introduce schema meta-matching, a novel framework for composing an arbitrary ensemble of algorithms for schema matching. Informally, schema meta-matching is about computing a "consensus" ranking of alternative mappings between two sets of concepts, given the "individual" graded rankings provided by several schema matching algorithms. We introduce several algorithms for this problem, varying from adaptations of some standard techniques for general quantitative rank aggregation, to novel techniques specific to the problem of schema matching, and to combinations of both.