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

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

12 April 2014
William Yang Wang
Kathryn Mazaitis
Ni Lao
Tom Michael Mitchell
William W. Cohen
    LRM
ArXiv (abs)PDFHTML

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

3 / 3 papers shown
Title
Intelligent Computing: The Latest Advances, Challenges and Future
Intelligent Computing: The Latest Advances, Challenges and Future
Shiqiang Zhu
Ting Yu
Tao Xu
Hongyang Chen
Schahram Dustdar
...
Tariq S. Durrani
Huaimin Wang
Jiangxing Wu
Tongyi Zhang
Yunhe Pan
AI4CE
87
130
0
21 Nov 2022
Prototypical Representation Learning for Relation Extraction
Prototypical Representation Learning for Relation Extraction
Ning Ding
Xiaobin Wang
Yao Fu
Guangwei Xu
Rui Wang
Pengjun Xie
Ying Shen
Fei Huang
Haitao Zheng
Rui Zhang
60
60
0
22 Mar 2021
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Fan Yang
Zhilin Yang
William W. Cohen
111
31
0
27 Feb 2017
1