@InProceedings{ojha-talukdar:2017:EMNLP2017,
  author    = {Ojha, Prakhar  and  Talukdar, Partha},
  title     = {KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1741--1750},
  abstract  = {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 uses coupling
	constraints to bind facts and crowdsources those few that can infer large parts
	of the graph. We demonstrate that the objective optimized by KGEval is
	submodular and NP-hard, allowing guarantees for our approximation algorithm.
	Through experiments on real-world datasets, we demonstrate that KGEval best
	estimates KG accuracy compared to other baselines, while requiring
	significantly lesser number of human evaluations.},
  url       = {https://www.aclweb.org/anthology/D17-1183}
}