— We propose a new approach to the problem of schema matching in relational databases that merges the hybrid and composite approach of combining multiple individual matching techniques. In particular, we propose assigning individual matchers to two categories, “strong” matchers that provide apriori higher quality matches, and “weak” matchers that may be more sensitive to the inputs and are less reliable but can still help generate some matches. Matching is correspondingly done in two phases, with strong “matches” being produced by strong matchers being combined using a simple voting combiner, and weak matchers providing additional evidence for attributes left unmatched (again using a voting combiner). We observe that, while many recent advances in schema matching [2][5][7][11] use composite schema matching and rely on the existence of training schemas to train combiners, in many real-world situations it is not feasible to employ learning techniques because of the unavaila...