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Markov Logic Network : Unify Framework for Ontology Learning

Real-world data is most often presented in inconsistent, noisy, or incomplete state. Probability is suitable framework for uncertainty and Logic theory is suitable for complexity. But Statistical Related learning based Markov Logic is suitable for both uncertainty and Complexity. The Markov logic is an advanced and encouraging method to handle this kind of uncertainty presented in the structured data. Markov logic joints the gap between the first order logic and then the probabilistic theory. A Markov logic network (MLN) is collection of first-order logic formulas called rules. This each rule is connected with a numerical weight. Markov logic provides facility to specify probability distributions among complex relational domains and Deterministic dependencies are specified by formulas with infinite weight.