Learning Probabilities for Noisy First-Order Rules

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Learning Probabilities for Noisy First-Order Rules (1997)

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by Daphne Koller , Avi Pfeffer

Venue:	In Proc. IJCAI
Citations:	45 - 0 self

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@INPROCEEDINGS{Koller97learningprobabilities,
    author = {Daphne Koller and Avi Pfeffer},
    title = {Learning Probabilities for Noisy First-Order Rules},
    booktitle = {In Proc. IJCAI},
    year = {1997},
    pages = {1316--1321},
    publisher = {Morgan Kaufmann}
}

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Abstract

First-order logic is the traditional basis for knowledge representation languages. However, its applicability to many real-world tasks is limited by its inability to represent uncertainty. Bayesian belief networks, on the other hand, are inadequate for complex KR tasks due to the limited expressivity of the underlying (propositional) language. The need to incorporate uncertainty into an expressive language has led to a resurgence of work on first-order probabilistic logic. This paper addresses one of the main objections to the incorporation of probabilities into the language: "Where do the numbers come from?" We present an approach that takes a knowledge base in an expressive rule-based first-order language, and learns the probabilistic parameters associated with those rules from data cases. Our approach, which is based on algorithms for learning in traditional Bayesian networks, can handle data cases where many of the relevant aspects of the situation are unobserved. It is also capabl...

Keyphrases

noisy first-order rule data case probabilistic parameter knowledge representation language expressive rule-based first-order language relevant aspect many real-world task traditional basis limited expressivity traditional bayesian network complex kr task first-order probabilistic logic bayesian belief network main objection knowledge base first-order logic expressive language

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