Programming with personalized pagerank: a locally groundable first-order probabilistic logic

@inproceedings{Wang2013ProgrammingWP,
  title={Programming with personalized pagerank: a locally groundable first-order probabilistic logic},
  author={William Yang Wang and Kathryn Mazaitis and William W. Cohen},
  booktitle={CIKM},
  year={2013}
}
Many information-management tasks (including classification, retrieval, information extraction, and information integration) can be formalized as inference in an appropriate probabilistic first-order logic. However, most probabilistic first-order logics are not efficient enough for realistically-sized instances of these tasks. One key problem is that queries are typically answered by "grounding" the query---i.e., mapping it to a propositional representation, and then performing propositional… CONTINUE READING

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