We address the problem of unsupervised matching of schema
information from a large number of data sources into the
schema of a data warehouse. The matching process is the
ﬁrst step of a framework to integrate data feeds from third-
party data providers into a structured-search engine’s data
warehouse. Our experiments show that traditional schema-
based and instance-based schema matching methods fall short.
We propose a new technique based on the search engine’s
clicklogs. Two schema elements are matched if the distribution of keyword queries that cause clickthroughs on their
instances are similar. We present experiments on large commercial datasets that show the new technique has much better accuracy than traditional techniques.