Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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First-Order Logic Generation and Weight Learning Method in Markov Logic Network Using Association Analysis

연관분석을 이용한 마코프 논리네트워크의 1차 논리 공식 생성과 가중치 학습방법

  • Ahn, Gil-Seung (Department of Industrial and Management Engineering, Hanyang University) ;
  • Hur, Sun (Department of Industrial and Management Engineering, Hanyang University)
  • 안길승 (한양대학교 산업경영공학과) ;
  • 허선 (한양대학교 산업경영공학과)
  • Received : 2014.12.26
  • Accepted : 2015.02.04
  • Published : 2015.03.31
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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.

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References

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