초록

Abstract

In this study, we investigated the information users' needs and information seeking behavior in the field of science and technology. We found that the science and technology researchers most frequently needed information while conducting the research & development related tasks and drafting research papers. The researchers attributed the main sources of research ideas to be the communication among colleagues and the literature review. The researchers's most preferred information sources were digital libraries, web search engines, and academic information portal. In conclusion, the science and technology researchers regarded the online use of electronic materials as the primary source of information. We hope that this investigation reported herein to be the foundation for developing user-centered information services for the science and technology discipline.

초록

Abstract

With the purpose of enriching existing catalogues with FRBR, which is the Functional Requirements for Bibliographic Records, in mind, this paper aims to evaluate the impact of bibliographic ontology on the overall system’s performance in the field of literature. In doing this, OCLC’s FictionFinder (http://fictionfinder.oclc.org) was selected and qualitatively evaluated. In this study 40 university seniors evaluated the following three aspects using the ‘transferring thoughts onto paper method’: 1) In which ways is this FRBR-aware bibliographical ontology helpful? 2) Are the things which are initially attempted to be helped being helped? 3) Would users seeking one work in particular also see all other related works? In conclusion, this study revealed that, as Cutter claimed in his 2nd rule of the library, collocations give added-value to the users and overall ontology provides better interface and usefulness. It also revealed that a system’s evaluation with qualitative methodology helped to build full pictures of the system and to grip the information needs of the users when the system is developed. Qualitative evaluations, therefore, could be used as indicators for the evaluation of any information retrieval systems.

초록

본 연구는 공공도서관과 대형서점 이용자의 요구 및 이용행태가 유사하다는 가정 하에 두 기관의 분류방식의 분석을 통해 이용자의 도서탐색을 용이하게 해주는 요인들을 조사하였다. 본 연구의 목적은 대형서점의 방식으로 공공도서관의 장서를 분류하는 것을 제안하기보다는 공공도서관과 대형서점 분류방식의 어떤 특징이 탐색을 용이하게 하는지를 알아보아, 공공도서관에서 보다 편리한 분류를 제공하기 위해 고려할 점들이 무엇인지를 알아보는데 있다. 이를 위해 먼저 공공도서관 이용자의 도서탐색의 특징과 공공도서관과 대형서점의 분류방식을 조사·분석한 후 설문지법을 통해 이용자의 도서탐색목적과 탐색영역, 탐색기준을 알아보고, 공공도서관과 대형서점의 분류표를 바탕으로 탐색에 더 편리한 분류방식과 그 원인을 분석하였다. 연구결과 이용자들은 대형서점의 분류방식이 도서탐색에 더 편리하다고 생각하고 있음이 밝혀졌다.

Abstract

The research was conducted to investigate factors that facilitate users’ finding books by analyzing classifications in public library and bookstore. This research was based on the assumption that the users’ needs and information behaviors are similar in both public library and bookstore. The main purpose of this study is not to recommend classifying public library collections the way a bookstore does, but to figure out what makes the users’ book finding more convenient by analyzing the classifications. To carry out the research, users’ book finding in public library and classifications of public library and bookstore are analyzed. Then, a survey was conducted to investigate users’ book finding behaviors, degree of convenience in finding books according to different classifications and the causes of the convenience. The results of the research showed that bookstore’s classification was more convenient for the users in finding books.

초록

최근 웹 환경이 대중화되고 개방됨에 따라 웹은 단순한 정보 획득의 공간이 아닌, 의견 표출과 교환의 장이 되어 가고 있으며, 이에 따라 웹 상에서 표출된 특정 주제에 대한 사람들의 의견을 자동으로 검색하기 위한 기술 개발의 필요성이 점차 증대되고 있다. 이러한 의견 문서 검색 문제는 사용자 질의와 문서간의 적합성만을 고려하는 일반적인 정보검색 방법으로는 해결하기 어려우며, 문서 내 의견 포함 여부 분석을 수행할 수 있는 더욱 진보된 시스템을 필요로 한다. 본 논문에서는 기존 검색 시스템의 구조 하에서, 의견 문서 검색을 효과적으로 수행할 수 있는 시스템을 제안한다. 의견 검색을 수행하기 위해 문서 내 의견 분석 방법에 대해 기존의 사전 기반 방식과 기계학습 기반 방식을 결합한 새로운 혼합 방식을 제안하고, 실험을 통하여 검색 성능을 개선하는 효과가 있음을 보였다.

Abstract

Recently, as its growth and popularization, the Web is changed into the place where people express, share and debate their opinions rather than the space of information seeking. Accordingly, the needs for searching opinions expressed in the Web are also increasing. However, it is difficult to meet these needs by using a classical information retrieval system that only concerns the relevance between the user's query and documents. Instead, a more advanced system that captures subjective information through documents is required. The proposed system effectively retrieves opinionated documents by utilizing an existing information retrieval system. This paper proposes a kind of hybrid method which can utilize both a dictionary-based opinion analysis technique and a machine learning based opinion analysis technique. Experimental results show that the proposed method is effective in improving the performance.