Making sense of top-k matchings
Avigdor Gal, Tomer Sagi, Matthias Weidlich, Eliezer Levy, Victor Shafran, Zoltán Miklós, Quoc Viet Hung Nguyen · 2012
Schema matching in uncertain environments faces several challenges, among them the identification of complex correspondences. In this paper, we present a method to address this challenge based on top-k matchings, i.e., a set of matchings comprising only 1: 1 correspondences derived by common matchers. We propose the unified top-k match graph and define a clustering problem for it. The obtained attribute clusters are analysed to derive complex correspondences. Our experimental evaluation shows that our approach is able to identify a significant share of complex correspondences.