정보과학회 논문지 (Journal of KIISE)

  • 제42권8호
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  • Pages.1065-1070
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  • 2015
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  • 2383-630X(pISSN)
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  • 2383-6296(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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위키백과로부터 기계학습 기반 한국어 지식베이스 구축

Construction of Korean Knowledge Base Based on Machine Learning from Wikipedia

  • 정석원 (강원대학교 컴퓨터정보통신공학과) ;
  • 최맹식 (강원대학교 컴퓨터정보통신공학과) ;
  • 김학수 (강원대학교 컴퓨터정보통신공학과)
  • 투고 : 2015.04.08
  • 심사 : 2015.05.26
  • 발행 : 2015.08.15
⟨ 이전 논문 다음 논문 ⟩

초록

지식베이스는 자연어 처리 기반의 다양한 응용 시스템 성능에 영향을 미치는 중요한 요소이다. 영어권에서는 WordNet, YAGO, Cyc, BabelNet과 같은 지식베이스들이 널리 사용되고 있다. 본 논문에서는 위키백과와 YAGO로부터 YAGO 형식의 한국어 지식베이스(이하 K-YAGO)를 자동 구축하는 방법을 제안한다. 제안 시스템은 YAGO와 위키백과 인포박스간의 간단한 매칭을 통해 초기 K-YAGO를 구축한 뒤, 기계학습을 이용하여 초기 K-YAGO를 확장한다. 실험 결과, 제안 시스템은 초기 K-YAGO 구축 실험에서 0.9642의 신뢰도를 보였고, K-YAGO 확장 실험에서 0.9468의 정확도와 0.7596의 매크로 F1 척도를 보였다.

The performance of many natural language processing applications depends on the knowledge base as a major resource. WordNet, YAGO, Cyc, and BabelNet have been extensively used as knowledge bases in English. In this paper, we propose a method to construct a YAGO-style knowledge base automatically for Korean (hereafter, K-YAGO) from Wikipedia and YAGO. The proposed system constructs an initial K-YAGO simply by matching YAGO to info-boxes in Wikipedia. Then, the initial K-YAGO is expanded through the use of a machine learning technique. Experiments with the initial K-YAGO shows that the proposed system has a precision of 0.9642. In the experiments with the expanded part of K-YAGO, an accuracy of 0.9468 was achieved with an average macro F1-measure of 0.7596.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

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