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http://dx.doi.org/10.11627/jkise.2014.38.1.74

First-Order Logic Generation and Weight Learning Method in Markov Logic Network Using Association Analysis  

Ahn, Gil-Seung (Department of Industrial and Management Engineering, Hanyang University)
Hur, Sun (Department of Industrial and Management Engineering, Hanyang University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.38, no.1, 2015 , pp. 74-82 More about this Journal
Abstract
Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.
Keywords
Statistical Relational Learning; Markov Logic Network; Association Rule; Knowledge-Based Model; First-Order Logic;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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