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.

1. 서 론
 2. 마코프 논리네트워크
  2.1 1차 논리
  2.2 마코프 네트워크
  2.3 모델 정의
  2.4 추론 및 학습
 3. 연관규칙
 4. 제안 알고리즘
  4.1 연관규칙 생성 및 주요 연관규칙 파악
  4.2 데이터간 유사도 계산 및 이웃 정의
  4.3 1차 논리 공식 생성 및 가중치 학습
  4.4 마코프 논리네트워크 구성
 5. 제안 알고리즘의 적용예와 결과 비교
  5.1 데이터
  5.2 결과
 6. 결 론
 Acknowledgements
 References

• 안길승(한양대학교 산업경영공학과) | Gil-Seung Ahn
• 허선(한양대학교 산업경영공학과) | Sun Hur Corresponding Author