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국내 과학기술콘텐츠 저자의 소속기관명 식별을 위한 소속기관명 자동 식별 알고리즘에 관한 연구

A Study on the Identification Algorithm for Organization's Name of Author of Korean Science & Technology Contents

  • Kim, Jinyoung (Korea Institute of Science and Technology Information) ;
  • Lee, Seok-Hyong (Korea Institute of Science and Technology Information) ;
  • Suh, Dongjun (Korea Institute of Science and Technology Information) ;
  • Kim, Kwang-Young (Korea Institute of Science and Technology Information) ;
  • Yoon, Jungsun (Korea Institute of Science and Technology Information)
  • 투고 : 2017.04.22
  • 심사 : 2017.04.28
  • 발행 : 2017.04.30

초록

과학기술콘텐츠가 증가함에 따라 과학기술콘텐츠의 효율적인 검색을 지원하는 서비스가 요구되고 있다. 저자의 소속기관명을 키워드로 사용할 경우 한 기관에서 생산된 콘텐츠를 확인할 수 있을 뿐만 아니라 저자, 용어를 키워드로 사용한 검색 결과의 식별율을 향상 시킬 수 있다. 검색 키워드로 사용되는 데이터들의 중의성과 모호성으로 인해 검색 결과에 false negative, false positive가 포함될 수 있으므로 데이터의 식별을 통한 통제는 중요하다. 저자의 소속기관명의 식별을 통한 통제 역시 기관의 이명, 약어 검색을 지원가능하게 하므로 매우 중요하지만 기존의 데이터 식별을 통한 통제에 대한 연구는 저자, 용어에 대한 연구가 주를 이루었다. 본 연구에서는 기관명 식별 알고리즘을 제안하고, 한국과학기술정보연구원에서 보유하고 있는 국내 과학기술콘텐츠들에 대한 데이터를 이용한 실험 결과를 보인다.

As the number of scientific and technical contents increases, services that support efficient search of scientific and technical contents are required. When an author's affiliation is used as a keyword, not only the contents produced by the affiliation can be searched, but also the identification rate of the search result using the author and the term as keyword can be improved. Because of the ambiguity and vagueness of the data used as a search keyword, the search result may include false negative or false positive. However, the previous research on the control through identification of the search keyword is mainly focused on the author data and terminology data. In this paper, we propose the algorithm to identify affiliations and experiment with show the experiment with scientific and technological contents held by the Korea Institute of Science and Technology Information.

키워드

참고문헌

  1. Sung Ho Shin, "An Approach of Organization's Name Authority Control for Improving Data Searching Results," Fall Conference, Korean Society for Internet Information, pp. 403-407, November 2008.
  2. In-Su Kang, Seungwoo Lee, Hanmin Jung, Pyung Kim, Heekwan Koo, Mi-Kyung Lee, Won-Kyung Sung, and Dong-In Park, "Features for Author Disambiguation," Journal of the Korea Contents Association, Vol. 8, No. 2, pp. 41-47, 2008. https://doi.org/10.5392/JKCA.2008.8.2.041
  3. Seok-Hyong Lee and Seung-Jin Kwak, "A Study on the Construction for Name Authority Data of the Korean Academic Papers," Journal of the Korean Biblia Society for Library and Information Science, Vol. 21, No. 1, pp. 105-118, March 2010.
  4. Seok-Hyong Lee and Seung-Jin Kwak, "Development and Evaluation of Authority Data based Academic Paper Retrieval System," Journal of the Society for Library and Information Science, Vol. 46, No. 2, pp. 133-156, May 2012.
  5. Seok-Hyoung Lee, "A Study on the Construction of Identified Data of Author's Affiliation in Academic Papers," Journal of the Institute for Social Sciences, Vol. 25, No. 4, pp. 391-410, 2014.
  6. Anderson A. Ferreira, Marcos Andre Goncalves and Alberto H. F. Laender, "A Brief Survey of Automatic Methods for Author Name Disambiguation," ACM SIGMOD Record, Vol. 41, No. 2, pp. 15-26, June, 2012. https://doi.org/10.1145/2350036.2350040
  7. Emiel Caron and Hennie Daniels, "Identification of Organization Name Variants in Large Databases using Rule-based Scoring and Clustering With a Case Study on the Web of Science Database," In Proc. of the 18th International Conference on Enterprise Information Systems(ICEIS 2016), Vol. 1, pp. 182-187, 2016.
  8. Jinyoung Kim, Seok-Hyong Lee, Dongjun Suh, and Kwang-Young Kim, "A Study on the Method and System for Organization's Name Authorization of Korean Science and Technology Contents," Journal of Digital Contents Society, Vol. 17, No. 6, pp. 555-563, Dec. 2016. https://doi.org/10.9728/dcs.2016.17.6.555

피인용 문헌

  1. 과학기술정보 서비스 지원을 위한 지식 공유 플랫폼 - 데이터, 기술 S/W 및 활용 사례를 중심으로 vol.18, pp.6, 2017, https://doi.org/10.9728/dcs.2017.18.6.1183
  2. 학술정보의 식별체계 현황 분석 및 연계 방안 연구 vol.51, pp.1, 2020, https://doi.org/10.16981/kliss.51.202003.115