most citedEfficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic

8 citations · 8 across the 1 of their papers we have counts for

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1 paper

cs.AI2014★ 8 cited

Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic

William Yang Wang, Kathryn Mazaitis, Ni Lao +2

One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced by modern web-scale information…