Browse > Article
http://dx.doi.org/10.16981/kliss.53.2.202206.95

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

Park, Jin Ho (한성대학교 크리에이티브 인문학부 도서관정보문화트랙)
Song, Min Sun (대림대학교 도서관미디어정보과)
Publication Information
Journal of Korean Library and Information Science Society / v.53, no.2, 2022 , pp. 95-115 More about this Journal
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.
Keywords
National Science and Technology Standard Classification System; Keyword Recommendation; Learning Machine Algorithm; Keyword Learning; AttentionMeSH;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
연도 인용수 순위
1 Noh, Young-Hee (2001). The study on the effective automatic classification of internet document using the machine learning. Journal of Korean Library and Information Science Society, 32(3), 307-330.
2 BioASQ (n.d.). Sixth Challenge Winners. Available: http://bioasq.org/participate/sixth-challenge-winners
3 Choi, Jong-Yun, Hahn, Hyuk, & Jung, Yu Chul (2020). Research on text classification of research reports using Korea national science and technology standards classification codes. Journal of the Korea Academia-Industrial Cooperation Society, 21(1), 169-177. http://10.5762/KAIS.2020.21.1.169   DOI
4 Kim, Hae Chan Sol, Ahn, Dae Jin, Yim, Jin Hee, & Rieh, Hae-Young (2017). A study on automatic classification of record text using machine learning. Journal of the Korean Society for Information Management, 34(4), 321-344. https://doi.org/10.3743/KOSIM.2017.34.4.321   DOI
5 Kim, Kwang-Young & Kwak, Seung-Jin (2010). A study of personalized retrieval system through society of Korean journal articles of science and technology. Journal of Korean Library and Information Science Society, 41(1), 149-165. http://10.16981/kliss.41.1.201003.149   DOI
6 Kim, Sunghee & Eom, Jae-Eun (2008). A study on the documents's automatic classification using machine learning. Journal of Information Management, 39(4), 47-66.   DOI
7 Kim, Kwang-Young & Kwak, Seung-Jin (2011). A study on personalized search system based on subject classification. Journal of the Korean Society for Library and Information Science, 45(4), 77-102. http://dx.doi.org/10.4275/KSLIS.2011.45.4.077   DOI
8 Korea Institute of S&T Evaluation and Planning (2019). A Study on Reestablishing the Role of KISTEP for Predicting the Future to Enhance the Utilization of Science and Technology Planning and Innovation Policy. Chung-cheong bukdo: Korea Institute of S&T Evaluation and Planning.
9 Lee, Soyoung & Chung, Young-Mee (2006). Design and evaluation of a personalized search service model based on web portal user activities. Journal of the Korean Society for Information Management, 23(4), 179-196. http://doi.org/10.3743/KOSIM.2006.23.4.179   DOI
10 Han, Hee-Jun, Choi, Yunsoo, & Choi, Sung-Pil (2018). A study on personalization of science and technology information by user interest tracking technique. Journal of the Korean Society for Library and Information Science, 52(3), 5-33. http://10.4275/KSLIS.2018.52.3.005   DOI
11 Song, Min Sun & Park, Jin Ho (2021). A study on development of SKOS-based metadata elements for managing keywords in the national science and technology standard classification system. Journal of the Korean Biblia Society for Library and Information Science, 32(4), 67-88. https://doi.org/10.14699/kbiblia.2021.32.4.067   DOI
12 Kim, Yunjeong, Shin, Donggu, & Jung, Hoikyung (2021). Recommendation system for research field of R&D project using machine learning. Journal of the Korea Institute of Information and Communication Engineering, 25(12), 1809-1816. http://doi.org/10.6109/jkiice.2021.25.12.1809   DOI
13 Cho, Hyun Yang (2017). A experimental study on the development of a book recommendation system using automatic classification, based on the personality type. Journal of Korean Library and Information Science Society, 48(2), 215-236. https://doi.org/10.16981/kliss.48.201706.215   DOI
14 Cho, Hyun Yang (2020). Design of the curation platform for user-participated book recommendation system of selecting on alternative material for the disabled. Journal of the Korean Society for Library and Information Science, 54(3), 41-69. https://doi.org/10.4275/KSLIS.2020.54.3.041   DOI
15 BioAsQ [n.d.]. Challenges - Tasks 6a, 6b - Year 6. Available: http://bioasq.org/participate/challenges_year_6
16 Ministry of Science and Technology Information and Communication (2019). Selection Results (Draft) for the Revision Feasibility Evaluation of the National Science and Technology Standards Classification System and the Long-term Improvement Direction. Ministry of Science and ICT, Performance Evaluation Policy Bureau, Science and Technology Information Division.
17 Jin, Q., Dhingra, B., Cohen, W., & Lu, X. (2018). Attentionmesh: simple, effective and interpretable automatic mesh indexer. Proceedings of the 6th BioASQ Workshop a Challenge on Large-scale Biomedical Semantic Indexing and Question Answering, 47-59. https://doi.org/10.18653/v1/W18-5306   DOI