Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Study on Information Retrieval of Web Using Local Context Analysts Feedback

지역적 문맥 분석 피드백을 이용한 웹 정보검색에 관한 연구

  • Published : 2004.10.01
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Abstract

In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM(Max and Min Model), Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly In this paper we propose a new soft evaluation method for web Information retrieval using local context analysis feedback model. We also show through performance comparison that local contort analysis feedback is more efficient and effective than MMM, Paice and P-norm.

순수한 부울 검색 시스템은 문서와 질의 사이의 유사 도를 나타내는 문서 값을 계산할 수 없기 때문에 검색된 문서들을 질의를 만족하는 정보에 따라 정렬할 수 없다. 부울 검색 시스템의 이러한 단점을 보완하는 방법으로 MMM 모델, Paice 모델 P-norm 모델이 개발되었다. 이러한 방법들은 부울 연산자를 유연하게 연산하는 공통된 특성을 지니고 있다. 본 논문에서는 높은 검색 효과를 제공하는 지역적 문맥 분석 피드백(Local Context Analysis Feedback)을 이용한 웹 정보 검색 모델을 이용한다. 지역적 문맥 분석 피드백 모델의 연산 특성이 MMM(Max and Min Model), Paice, p-norm 모델보다 우수함을 설명하고, 또한 성능 비교를 통하여 이를 입증한다.

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