초록

본 연구에서 제안하는 기법은 최대 개념강도 인지기법(Maximal Concept-Strength Recognition Method: MCR)이다. 신규 데이터베이스가 입수되어 자동분류가 필요한 경우에, 기 구축된 여러 데이터베이스 중에서 최적의 데이터베이스가 어떤 것인지 알 수 없는 상태에서 MCR 기법은 가장 유사한 데이터베이스를 선택할 수 있는 방법을 제공한다. 실험을 위해 서로 다른 4개의 학술 데이터베이스 환경을 구성하고 MCR 기법을 이용하여 최고의 성능값을 측정하였다. 실험 결과, MCR을 이용하여 최적의 데이터베이스를 정확히 선택할 수 있었으며 MCR을 이용한 자동분류 정확률도 최고치에 근접하는 결과를 보여주었다.

Abstract

The proposed method in this study is the Maximal Concept-Strength Recognition Method(MCR). In case that we don't know which database is the most suitable for automatic-classification when new database is imported, MCR method can support to select the most similar database among many databases in the legacy system. For experiments, we constructed four heterogeneous scholarly databases and measured the best performance with MCR method. In result, we retrieved the exact database expected and the precision value of MCR based automatic-classification was close to the best performance.

초록

Abstract

The purpose of this study is to investigate image search query reformulation patterns in relation to image attribute categories. A total of 592 sessions and 2,445 queries from the Excite Web search engine log data were analyzed by utilizing Batley’s visual information types and two facets and seven sub-facets of query reformulation patterns. The results of this study are organized with two folds: query reformulation and categorical transition. As the most dominant categories of queries are specific and general/nameable, this tendency stays over various search stages. From the perspective of reformulation patterns, while the Parallel movement is the most dominant, there are slight differences depending on initial or preceding query categories. In examining categorical transitions, it was found that 60-80% of search queries were reformulated within the same categories of image attributes. These findings may be applied to practice and implementation of image retrieval systems in terms of assisting users’ query term selection and effective thesauri development.

초록

Abstract

In the current information environment, metadata interoperability has become the predominant way of organizing and managing resources. However, current approaches to metadata interoperability focus on the superficial mapping between labels of metadata elements without considering semantics of each element. This research applied facet analysis to address these difficulties in achieving metadata interoperability. By categorizing metadata elements according to these semantic and functional similarities, this research identified different types of facets: basic, conceptual, and relational. Through these different facets, a faceted framework was constructed to mediate semantic, syntactical, and structural differences across heterogeneous metadata standards.

초록

이 연구는 2000년 이후 발표된 정보활용능력 분야의 국내 학위논문을 분석함으로써, 양적연구의 동향과 흐름을 분석하였다. 이를 위해 양적연구 동향을 분석하였다. 양적 연구를 위해 양적 연구 과정과 양적조사 관련 규정, 양적연구 관련 기술요소를 비교분석하였다. 또한, 각 요소 측정을 위해 5개 변인과 기준을 사용하였다. 이를 바탕으로 논문에 대한 연구주제, 조사방법, 표집방법, 표본대상, 표본크기에 대한 일반적 특징을 살펴보고, 이를 연도별, 전공별로 구분하여 측정변인에 대한 동향을 분석하였다. 또한 연구에서 사용한 통계분석방법을 목적에 따라 분류하여 연구목적에 따른 통계분석기법의 사용동향을 제시하였다.

Abstract

This study has analyzed the trend of the quantitative research by analyzing domestic dissertations on information literacy that were published since 2000. The procedures, regulations, and descriptive elements of the quantitative study were compared and analyzed for this study. In addition, the study used 5 variables and criterions to measure these items. Based on the calculations, the study has examined the general characteristics of the thesis, research method, sampling method and sampling population of the dissertations. The study has also analyzed the trend of the measurement variables by categorizing the characteristics by published year and majors. Furthermore, the study has also presented the trend of the usage of statistic analysis method on research purpose by classifying the method into each purpose.