  Existing techniques for unionable table search define unionability using
metadata (tables must have the same or similar schemas) or column-based metrics
(for example, the values in a table should be drawn from the same domain). In
this work, we introduce the use of semantic relationships between pairs of
columns in a table to improve the accuracy of union search. Consequently, we
introduce a new notion of unionability that considers relationships between
columns, together with the semantics of columns, in a principled way. To do so,
we present two new methods to discover semantic relationship between pairs of
columns. The first uses an existing knowledge base (KB), the second (which we
call a "synthesized KB") uses knowledge from the data lake itself. We adopt an
existing Table Union Search benchmark and present new (open) benchmarks that
represent small and large real data lakes. We show that our new unionability
search algorithm, called SANTOS, outperforms a state-of-the-art union search
that uses a wide variety of column-based semantics, including word embeddings
and regular expressions. We show empirically that our synthesized KB improves
the accuracy of union search by representing relationship semantics that may
not be contained in an available KB. This result hints at a promising future of
creating a synthesized KBs from data lakes with limited KB coverage and using
them for union search.
