Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Hybrid Information Retrieval Model Using Metadata and Text

메타데이타와 텍스트 정보의 통합검색 모델

  • 유정목 (한국전자통신연구원 디지털홈연구단 인터넷서버그룹) ;
  • 맹성현 (한국정보통신대학교 공학부) ;
  • 김성수 (한국통신 비지니스부문 프로젝트 관리부) ;
  • 이만호 (충남대학교 전기정보통신공학부)
  • Published : 2007.06.15
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Abstract

Metadata IR model has high precision and low recall because the query in Metadata IR model is strict that is, the query can express user information need exactly, while Full-text IR model has low precision and high recall because the query in Full-text IR model is a kind of simple keyword query which expresses user information need roughly. If user can translate one's information need into structured query well, the retrieval result will be improved. However, it is little possible to make relevant query without understanding characteristics of metadata. Unfortunately, most users do not interested in metadata, then they cannot construct well-made structured query. Amount of information contained in metadata is less than text information. In this paper, we suggest hybrid IR model using metadata and text which can provide users with lots of relevant documents by retrieving from metadata field and text field complementarily.

메타데이타를 위한 검색모델은 질의에 사용자의 정보요구를 정확하게 반영하기 때문에 정확율(precision)은 높지만 질의 조건에 만족하지 않는 정보를 배제하므로 재현율(recall)은 낮다. 반면 전문(full-text) 텍스트 검색 모델은 사용자 질의에 대하여 모든 문서를 검색대상으로 하므로 정확율은 낮고 재현율은 높다. 메타데이타 검색모델의 높은 정확율은 사용자가 메타데이타의 구조적 특성에 맞게 질의를 구성할 경우 가능하지만 일반적으로 사용자가 메타데이타의 구조 정보를 반영한 사용자 질의를 구성할 수 있다고 기대하기는 어렵다. 또한 메타데이타에 포함된 정보의 양은 전문 텍스트가 가진 정보의 양보다 적기 때문에 텍스트를 검색한 결과보다 재현율이 떨어진다. 본 논문에서는 이러한 특성을 반영하여 메타데이타 검색 시, 사용자의 다양한 질의를 메타데이타의 특성에 맞게 재구성하고 메타데이타뿐 아니라 텍스트에 대해서도 검색을 수행하여 두 모델의 장점을 함께 고려한 통합 검색 모델을 제안한다.

Keywords

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