document: Making holistic schema matching robust : an ensemble approach The Web has been rapidly `` deepened '' by myriad searchable databases online , where data are hidden behind query interfaces . As an essential task toward integrating these massive `` deep Web '' sources , large scale schema matching ( i.e. , discovering semantic correspondences of attributes across many query interfaces ) has been actively studied recently . In particular , many works have emerged to address this problem by `` holistically '' matching many schemas at the same time and thus pursuing `` mining '' approaches in nature . However , while holistic schema matching has built its promise upon the large quantity of input schemas , it also suffers the robustness problem caused by noisy data quality . Such noises often inevitably arise in the automatic extraction of schema data , which is mandatory in large scale integration . For holistic matching to be viable , it is thus essential to make it robust against noisy schemas . To tackle this challenge , we propose a data-ensemble framework with sampling and voting techniques , which is inspired by bagging predictors . Specifically , our approach creates an ensemble of matchers , by randomizing input schema data into many independently downsampled trials , executing the same matcher on each trial and then aggregating their ranked results by taking majority voting . As a principled basis , we provide analytic justification of the effectiveness of this data-ensemble framework . Further , empirically , our experiments on real Web data show that the `` ensemblization '' indeed significantly boosts the matching accuracy under noisy schema input , and thus maintains the desired robustness of a holistic matcher .
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