• Title/Summary/Keyword: 계량서지학 분석

Search Result 123, Processing Time 0.016 seconds

A Study on the Research Trends in Library & Information Science in Korea using Topic Modeling (토픽모델링을 활용한 국내 문헌정보학 연구동향 분석)

  • Park, Ja-Hyun;Song, Min
    • Journal of the Korean Society for information Management
    • /
    • v.30 no.1
    • /
    • pp.7-32
    • /
    • 2013
  • The goal of the present study is to identify the topic trend in the field of library and information science in Korea. To this end, we collected titles and s of the papers published in four major journals such as Journal of the Korean Society for information Management, Journal of the Korean Society for Library and Information Science, Journal of Korean Library and Information Science Society, and Journal of the Korean BIBLIA Society for library and Information Science during 1970 and 2012. After that, we applied the well-received topic modeling technique, Latent Dirichlet Allocation(LDA), to the collected data sets. The research findings of the study are as follows: 1) Comparison of the extracted topics by LDA with the subject headings of library and information science shows that there are several distinct sub-research domains strongly tied with the field. Those include library and society in the domain of "introduction to library and information science," professionalism, library and information policy in the domain of "library system," library evaluation in the domain of "library management," collection development and management, information service in the domain of "library service," services by library type, user training/information literacy, service evaluation, classification/cataloging/meta-data in the domain of "document organization," bibliometrics/digital libraries/user study/internet/expert system/information retrieval/information system in the domain of "information science," antique documents in the domain of "bibliography," books/publications in the domain of "publication," and archival study. The results indicate that among these sub-domains, information science and library services are two most focused domains. Second, we observe that there is the growing trend in the research topics such as service and evaluation by library type, internet, and meta-data, but the research topics such as book, classification, and cataloging reveal the declining trend. Third, analysis by journal show that in Journal of the Korean Society for information Management, information science related topics appear more frequently than library science related topics whereas library science related topics are more popular in the other three journals studied in this paper.

Bibliometric Analysis of Traditional Korean Medical Journals Registered with the National Research Foundations of Korea (한국연구재단에 등재된 한의학 학술지에 대한 계량서지학적 비교분석 연구)

  • Yea, Sang-Jun;Kim, Chul;Kim, Sang-Kyun;Jang, Hyun-Chul;Kim, Jin-Hyun;Jeon, Byoung-Uk;Jang, Yun-Ji;Seong, Bo-Seok;Song, Mi-Young
    • The Journal of Korean Medicine
    • /
    • v.32 no.5
    • /
    • pp.66-77
    • /
    • 2011
  • Objectives: This study aimed to make preliminary data through the bibliometric analysis of journals registered by national research foundations of Korea for the improvement of traditional Korean medical (TKM) journals. Methods: We collected 4,396 articles from the OASIS database, which is composed of articles published by TKM societies and institutes. First, we analyzed the 'authors by year', 'average authors per article', 'articles by institute' and 'authors by institute' to get the author-related conditions. Second, we analyzed the 'reference type', 'cited times', 'IF with self citation' and 'IF without self citation' to get the citation condition. Results: First, we found that the journal order of total authors was KAOOMP (2362), KOMS (1189), and KAMS (967), and of average authors per article was KAMS (5.29), KOIMS (5.25), and KOMS (4.75). Second, we learned that the journal order of occupied article ratio by high rank institutes was SCMS (92.4%), MAS (90.03%), and KOPS (87.22%) and of occupied author ratio by high rank institutes was KOPMS (96.55%), MAS (95.19%), and SCMS (93.85%). Third, we analyzed the most highly cited reference type by journals and we found that OMCS was books, SCMS was oriental medical journals and the other 10 journals were not oriental medical journals. Finally, we observed that the journal order of self citation ratio was SCMS (16.79%), KMAS (11.77%), and OOGS (11.67%) and also that the IF order was KAMS (0.675), OOGS (0.546), and KAOH (0.430). Conclusions: Through this study we found that TKM research leans too much toward on oriental medical universities, so we insist that TKM R&D institutes must be expanded. We also found that the self citation ratio was high in TKM journals, so the ratio must be decreased to improve the quality of the TKM journals.

Predicting Performance of Heavy Industry Firms in Korea with U.S. Trade Policy Data (미국 무역정책 변화가 국내 중공업 기업의 경영성과에 미치는 영향)

  • Park, Jinsoo;Kim, Kyoungho;Kim, Buomsoo;Suh, Jihae
    • The Journal of Society for e-Business Studies
    • /
    • v.22 no.4
    • /
    • pp.71-101
    • /
    • 2017
  • Since late 2016, protectionism has been a major trend in world trade with the Great Britain exiting the European Union and the United States electing Donald Trump as the 45th president. Consequently, there has been a huge public outcry regarding the negative prospects of heavy industry firms in Korea, which are highly dependent upon international trade with Western countries including the United States. In light of such trend and concerns, we have tried to predict business performance of heavy industry firms in Korea with data regarding trade policy of the United States. United States International Trade Commission (USITC) levies countervailing duties and anti-dumping duties to firms that violate its fair-trade regulations. In this study, we have performed data analysis with past records of countervailing duties and anti-dumping duties. With results from clustering analysis, it could be concluded that trade policy trends of the Unites States significantly affects the business performance of heavy industry firms in Korea. Furthermore, we have attempted to quantify such effects by employing long short-term memory (LSTM), a popular neural networks model that is well-suited to deal with sequential data. Our major contribution is that we have succeeded in empirically validating the intuitive argument and also predicting the future trend with rigorous data mining techniques. With some improvements, our results are expected to be highly relevant to designing regulations regarding heavy industry in Korea.