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연관분석을 이용한 마코프 논리네트워크의1차 논리 공식 생성과 가중치 학습방법First-Order Logic Generation and Weight Learning Method in Markov Logic Network Using Association Analysis
- Other Titles
- First-Order Logic Generation and Weight Learning Method in Markov Logic Network Using Association Analysis
- Issue Date
- Mar-2015
- Publisher
- 한국산업경영시스템학회
- Keywords
- Statistical Relational Learning; Markov Logic Network; Association Rule; Knowledge-Based Model; First-Order Logic
- Citation
- 한국산업경영시스템학회지, v.38, no.1, pp 74 - 82
- Pages
- 9
- Indexed
- KCI
- Journal Title
- 한국산업경영시스템학회지
- Volume
- 38
- Number
- 1
- Start Page
- 74
- End Page
- 82
- ISSN
- 2005-0461
2287-7975
- 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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