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- W2595139258 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 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." @default.
- W2595139258 created "2017-03-23" @default.
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- W2595139258 date "2016-10-21" @default.
- W2595139258 modified "2023-09-27" @default.
- W2595139258 title "KGEval: Estimating Accuracy of Automatically Constructed Knowledge Graphs" @default.
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