2006 MarkovLogicNetworks

• (Richardon & Domingos, 2006) ⇒ Matthew Richardson, Pedro Domingos. (2006). “Markov Logic Networks.” In: Machine Learning, 62. doi:10.1007/s10994-006-5833-1.

Subject Headings: Markov Logic Network, Global Collective Classification Algorithm.

Cited By

• ~390 http://scholar.google.com/scholar?cites=2300874061023306313

Quotes

Keyowords

• Statistical relational learning - Markov networks - Markov random fields - Log-linear models - Graphical models - First-order logic - Satisfiability - Inductive logic programming - Knowledge-based model construction - Markov chain Monte Carlo - Pseudo-likelihood - Link prediction

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 MarkovLogicNetworks	Pedro Domingos Matthew Richardson			Markov Logic Networks		Machine Learning (ML) Subject Area	http://www.cs.washington.edu/homes/pedrod/kbmn.pdf	10.1007/s10994-006-5833-1		2006