Journal of the Korean Society for Library and Information Science (한국문헌정보학회지)

Korean Society For Library And Information Science (한국문헌정보학회)
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An Exploratory Study on the Construction of Retrospective Authority Data with National Authorities

국가전거를 활용한 단위 도서관의 전거데이터 소급 구축 연구

  • 노지현 (부산대학교 문헌정보학과)
  • Received : 2023.01.16
  • Accepted : 2023.02.08
  • Published : 2023.02.28
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Abstract

In the recent cataloging environment, the function of authority data is becoming more important. In Korea, a pilot project for sharing and joint use of the national authority data has been promoted since 2019, and a plan to automatically construct retrospective authority data using national authorities is in progress. This study aims to (1) analyze the results of the pilot project for retrospective authority data jointly promoted by the National Library of Korea and KERIS in 2019, (2) explore methodological limitations and problems in the process, and (3) derive policy proposals for constructing authority data more efficiently. To this end, a university library participating in the pilot project was investigated as a case study. Based on it, the automatic generation rate and accuracy of authority data, and errors and omissions cases and causes were analyzed in detail. As a results of the study, it was found that the quality of union catalogs and bibliographic records had a great influence on automatically constructing authority data, and supplementary methods were needed to minimize these limitations.

최근의 목록 환경에서 전거데이터의 기능이 특히 중요해지고 있다. 이에 따라 국내에서는 2019년부터 국가전거데이터의 공개와 공동 활용을 위한 시범사업을 추진하면서 국가전거를 활용한 단위 도서관의 전거데이터 소급 구축 방안을 모색하고 있다. 이 연구에서는 지난 2019년 국립중앙도서관과 KERIS에서 공동으로 추진했던 전거데이터 소급 구축을 위한 시범사업의 방식과 결과를 분석하고, 이 과정에서 나타난 방법론적 한계와 문제점을 분석한 후 보다 많은 도서관들이 국가전거를 활용하여 전거데이터의 소급 구축을 효율적으로 추진할 수 있도록 정책적 제언을 도출하는데 목적을 두었다. 이를 위해 시범사업에 참여한 A대학도서관을 사례로 하여, 전거데이터의 자동 생성률과 정확률, 그리고 자동 생성 과정에서 나타난 오류와 누락의 사례와 원인을 분석하였다. 연구결과, 국가전거를 이용한 전거데이터의 자동생성률은 20% 남짓에 불과했고, 전거형의 형식에 따라 편차가 큰 것으로 나타났다. 이러한 낮은 생성률과 오류, 누락은 종합목록이나 단위 도서관의 서지레코드 품질에서 비롯된 것이었고, 이에 따라 기존 데이터에 대한 보다 세밀한 검토와 함께 정교한 알고리즘을 개발, 적용할 필요가 있음을 확인할 수 있었다.

Keywords

Acknowledgement

이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.

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