Table union search on open data

Abstract

We define the table union search problem and present a probabilistic solution for finding tables that are unionable with a query table within massive repositories. Two tables are unionable if they share attributes from the same domain. Our solution formalizes three statistical models that describe how unionable attributes are generated from set domains, semantic domains with values from an ontology, and natural language domains. We propose a data-driven approach that automatically determines the best model to use for each pair of attributes. Through a distribution-aware algorithm, we are able to find the optimal number of attributes in two tables that can be unioned. To evaluate accuracy, we created and open-sourced a benchmark of Open Data tables. We show that our table union search outperforms in speed and accuracy existing algorithms for finding related tables and scales to provide efficient search over Open Data repositories containing more than one million attributes.