Markov Logic Networks

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Markov Logic Networks (2006)

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by Matthew Richardson , Pedro Domingos

Venue:	MACHINE LEARNING
Citations:	816 - 39 self

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@INPROCEEDINGS{Richardson06markovlogic,
    author = {Matthew Richardson and Pedro Domingos},
    title = {Markov Logic Networks},
    booktitle = {MACHINE LEARNING},
    year = {2006},
    pages = {2006},
    publisher = {}
}

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Abstract

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

Keyphrases

markov logic network first-order formula corresponding weight additional clause real-world database probabilistic graphical model ground network simple approach minimal subset university domain single representation relational database ground markov network possible grounding first-order knowledge base pseudo-likelihood measure knowledge base first-order logic inductive logic programming technique

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