  Data integration is a notoriously difficult and heuristic-driven process,
especially when ground-truth data are not readily available. This paper
presents a measure of uncertainty by providing maximal and minimal ranges of a
query outcome in two-table, one-to-many data integration workflows. Users can
use these query results to guide a search through different matching
parameters, similarity metrics, and constraints. Even though there are
exponentially many such matchings, we show that in appropriately constrained
circumstances that this result range can be calculated in polynomial time with
bipartite graph matching. We evaluate this on real-world datasets and synthetic
datasets, and find that uncertainty estimates are more robust when a
graph-matching based approach is used for data integration.
