지능정보연구 (Journal of Intelligence and Information Systems)

  • 제17권4호
  • /
  • Pages.19-36
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  • 2011
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  • 2288-4866(pISSN)
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  • 2288-4882(eISSN)
한국지능정보시스템학회 (Korea Intelligent Information System Society)
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시맨틱 검색 : 서베이

Semantic Search : A Survey

  • 박진수 (서울대학교 경영대학/경영전문대학원) ;
  • 김남원 (서울대학교 경영대학) ;
  • 최민정 (서울대학교 경영대학) ;
  • 김철 (서울대학교 경영대학) ;
  • 최영석 (서울대학교 경영대학)
  • Park, Jin-Soo (College of Business Administration & Graduate School of Business, Seoul National University) ;
  • Kim, Nam-Won (College of Business Administration, Seoul National University) ;
  • Choi, Min-Jung (College of Business Administration, Seoul National University) ;
  • Jin, Zhe (College of Business Administration, Seoul National University) ;
  • Choi, Young-Seok (College of Business Administration, Seoul National University)
  • 투고 : 2011.12.05
  • 심사 : 2011.12.20
  • 발행 : 2011.12.31
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초록

시맨틱 웹(Semantic Web)의 비전에 대한 공표가 이루어진 이래로 이와 관련한 많은 연구가 진행되어 왔다. 그러나 지금까지의 연구가 성공적이었다는 판단을 하기에는 아직 이르다. 본 논문은 시맨틱 관련 연구분야의 두 가지 문제점을 진단한다. 첫째는 '시맨틱 검색'이라는 개념의 합의된 정의가 없다는 것이고, 둘째는 장래의 유관 연구를 바라볼 수 있는 종합적이고 체계적인 시각이 부족하다는 것이다. 이러한 진단 아래, 본 논문은 시맨틱 검색의 개념을 '사용자의 입력에 따라 온톨로지와 같은 시맨틱 기술을 이용하여 원하는 정보를 얻는 행위'로 정의한다. 또한 시맨틱 검색에 대한 이해를 돕기 위해 시맨틱 검색 엔진 분류 프레임워크를 제안하였다. 본 연구에서 제안하는 프레임워크는 (쿼리) 입력문의 처리, 타겟 소스, 검색 방법론, 검색결과의 서열화, 출력 결과물의 데이터 종류, 이렇게 다섯 가지 부분으로 나뉜다. 마지막으로 본 논문은 제시한 프레임워크를 응용하여 기존의 연구결과물을 분석하고 앞으로의 연구 방향을 논하는 것으로 끝을 맺는다.

Since the ambitious declaration of the vision of the Semantic Web, a growing number of studies on semantic search have recently been made. However, we recognize that our community has not so much accomplished despite those efforts. We analyze two underlying problems : a lack of a shared notion of semantic search that guides current research, and a lack of a comprehensive view that envisions future work. Based on this diagnosis, we start by defining semantic search as the process of retrieving desired information in response to user's input using semantic technologies such as ontologies. Then, we propose a classification framework in order for the community to obtain the better understanding of semantic search. The proposed classification framework consists of input processing, target source, search methodology, results ranking, and output data type. Last, we apply our proposed framework to prior studies and suggest future research directions.

키워드

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