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A Study on Automatic Recommendation of Keywords for Sub-Classification of National Science and Technology Standard Classification System Using AttentionMesh

  • Journal of Korean Library and Information Science Society
  • Abbr : JKLISS
  • 2022, 53(2), pp.95-115
  • DOI : 10.16981/kliss.53.2.202206.95
  • Publisher : Korean Library And Information Science Society
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : May 25, 2022
  • Accepted : June 21, 2022
  • Published : June 30, 2022

Park Jin Ho 1 Song Min Sun 2

1한성대학교
2대림대학교

Accredited

ABSTRACT

The purpose of this study is to transform the sub-categorization terms of the National Science and Technology Standards Classification System into technical keywords by applying a machine learning algorithm. For this purpose, AttentionMeSH was used as a learning algorithm suitable for topic word recommendation. For source data, four-year research status files from 2017 to 2020, refined by the Korea Institute of Science and Technology Planning and Evaluation, were used. For learning, four attributes that well express the research content were used: task name, research goal, research abstract, and expected effect. As a result, it was confirmed that the result of MiF 0.6377 was derived when the threshold was 0.5. In order to utilize machine learning in actual work in the future and to secure technical keywords, it is expected that it will be necessary to establish a term management system and secure data of various attributes.

Citation status

* References for papers published after 2022 are currently being built.