Finding Related Tables

We consider the problem of finding related tables in a large corpus of
heterogenous tables. Detecting related tables provides users a
powerful tool for enhancing their tables with additional data and
enables effective reuse of available public data. Our first
contribution is a framework that captures several types of relatedness,
including tables that are candidates for joins and tables that are
candidates for union. Our second contribution is a set of algorithms
for detecting related tables that can be either unioned or
joined. We describe a set of experiments that demonstrate that our
algorithms produce highly related tables. We also show that
we can often improve the results of table search by
pulling up tables that are ranked much lower based on their
relatedness to top-ranked tables. Finally, we describe how to scale up
our algorithms and show the results of running it on a corpus of over
a million tables extracted from Wikipedia.

• Data Management