  Automatic construction of large knowledge graphs (KG) by mining web-scale
text datasets has received considerable attention recently. Estimating accuracy
of such automatically constructed KGs is a challenging problem due to their
size and diversity. This important problem has largely been ignored in prior
research we fill this gap and propose KGEval. KGEval binds facts of a KG using
coupling constraints and crowdsources the facts that infer correctness of large
parts of the KG. We demonstrate that the objective optimized by KGEval is
submodular and NP-hard, allowing guarantees for our approximation algorithm.
Through extensive experiments on real-world datasets, we demonstrate that
KGEval is able to estimate KG accuracy more accurately compared to other
competitive baselines, while requiring significantly lesser number of human
evaluations.
