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2013 | OriginalPaper | Chapter
Knowledge Discovery from Constrained Relational Data: A Tutorial on Markov Logic Networks
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This tutorial paper gives an overview of Markov logic networks (MLNs) in theory and in practice. The basic concepts of MLNs are introduced in a semi-formal way and examined for their significance in the broader context of statistical relational learning approaches in general and Bayesian logic networks in particular. A sandbox example is discussed in order to explain in detail the meanings of input theories with weighted clauses for a MLN. Then, the setup needed for real-world applications using a recent open source prototype is introduced. Processing steps of inferencing and learning are explained in detail together with the best scaling algorithms known today. An overview on existing and upcoming application areas concludes the paper.
