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

    Machine Learning, Volume abs/1404.3301, 2014.

    Cited by: 34|Bibtex|Views40|Links
    EI

    Abstract:

    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 extraction systems. One scalability problem shared by many probabilistic logics is that answering queries involves "grounding" the query---i.e., mapping it t...More

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