• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.63-77
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• 2011

The World Wide Web is a very large distributed digital information space. From its origins in 1991, the web has grown to encompass diverse information resources as personal home pasges, online digital libraries and virtual museums. Some estimates suggest that the web currently includes over 500 billion pages in the deep web. The ability to search and retrieve information from the web efficiently and effectively is an enabling technology for realizing its full potential. With powerful workstations and parallel processing technology, efficiency is not a bottleneck. In fact, some existing search tools sift through gigabyte.syze precompiled web indexes in a fraction of a second. But retrieval effectiveness is a different matter. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Furthermore, the most relevant documents do not nessarily appear at the top of the query output order. Also, current search tools can not retrieve the documents related with retrieved document from gigantic amount of documents. The most important problem for lots of current searching systems is to increase the quality of search. It means to provide related documents or decrease the number of unrelated documents as low as possible in the results of search. For this problem, CiteSeer proposed the ACI (Autonomous Citation Indexing) of the articles on the World Wide Web. A "citation index" indexes the links between articles that researchers make when they cite other articles. Citation indexes are very useful for a number of purposes, including literature search and analysis of the academic literature. For details of this work, references contained in academic articles are used to give credit to previous work in the literature and provide a link between the "citing" and "cited" articles. A citation index indexes the citations that an article makes, linking the articleswith the cited works. Citation indexes were originally designed mainly for information retrieval. The citation links allow navigating the literature in unique ways. Papers can be located independent of language, and words in thetitle, keywords or document. A citation index allows navigation backward in time (the list of cited articles) and forwardin time (which subsequent articles cite the current article?) But CiteSeer can not indexes the links between articles that researchers doesn't make. Because it indexes the links between articles that only researchers make when they cite other articles. Also, CiteSeer is not easy to scalability. Because CiteSeer can not indexes the links between articles that researchers doesn't make. All these problems make us orient for designing more effective search system. This paper shows a method that extracts subject and predicate per each sentence in documents. A document will be changed into the tabular form that extracted predicate checked value of possible subject and object. We make a hierarchical graph of a document using the table and then integrate graphs of documents. The graph of entire documents calculates the area of document as compared with integrated documents. We mark relation among the documents as compared with the area of documents. Also it proposes a method for structural integration of documents that retrieves documents from the graph. It makes that the user can find information easier. We compared the performance of the proposed approaches with lucene search engine using the formulas for ranking. As a result, the F.measure is about 60% and it is better as about 15%.

• Journal of the Korean Society for information Management
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• v.24 no.4
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• pp.305-321
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• 2007

The purpose of this study is to examine whether the information resources referenced by human indexers during indexing process are effective on Text Categorization. More specifically, information resources from bibliographic information as well as full text information were explored in the context of a typical scientific journal article data set. The experiment results pointed out that information resources such as citation, source title, and title were not significantly different with full text. Whereas keyword was found to be significantly different with full text. The findings of this study identify that information resources referenced by human indexers can be considered good candidates for text categorization for automatic subject term assignment.

• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.1-21
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• 2003

The purpose of this study is to analysis the types of cited materials dependence ratio of foreign information of researchers, and half-life of some cited analysis, Journal articles from four science & technology fields-mechanical, architectural, chemical, electrical-are selected, and the literatures cited by those journal articles are analysed in terms of resource types, languages, publication year of cited analysis. In result, it was found that the order of frequency of citation is scholarly journal, monograph, Proceeding, technical report. And dependence ratio of foreign information of researchers was most higher in the chemical field. Also, it was found that half-life of mechanical is 6.50, that of architectural is 5.45, that of chemical is 9.65, that of electrical is 5.60

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1171-1186
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• 2011

Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.

• Journal of Information Science Theory and Practice
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• v.8 no.4
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• pp.40-54
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• 2020

This proposed work aims to understand the Korean Academic Circle (KAC)'s research trend on the "One Belt One Road" (OBOR) by employing a quantitative analysis of the recent research articles published by the KAC. To do so, this proposed research has used the well-known network analysis software, Ucinet 6, by which the papers on related topics are collected and filtered from Korea Citation Index. To perform the analytical selection, the proposed work has chosen 'keywords' as the core research object and performed analysis from transverse to longitudinal aspects, and from holistic to individual aspects, respectively; and from this, the KAC's research trend on OBOR is derived. The present work has established that the KAC's attention is continuously increasing on OBOR and has sustainability. Centered on the OBOR, Korean researchers have spread their studies in various dimensions ranging from the issues like China's political economy to Sino-Korea economic and trade exchanges, and so on. The KAC has even combined OBOR with Korea's international development initiatives, which can help Korea benefit from active and sustainable cooperation with China. Moreover, the proposed work has found that Korean researchers have also actively expressed their growing attention, highlighted Korea's interest, and showed concern about China hegemony and Sinocentrism in their recent documented research works.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.4
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• The Korea Journal of Herbology
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• v.28 no.5
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• pp.29-31
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• 2013

Objectives : 'Reconsideration about Nomenclature of Herbs Listed in the Korean Pharmacopoeia' was published by Doh and Lee with absolute misconception of nomenclature. A critical review of Doh and Lee's paper is given, to correct the confused the concept of nomenclature and to provide proper scientific name for taxa which are discussed. Methods : This paper discusses the proper usage, as mandated by the International Code of Nomenclature. Adherence to the rules described in this paper should reduce the present confusion in the nomenclature of scientific names listed in the Korean Pharmacopoeia. Results : Although Doh and Lee proposed four categories to correct the scientific names of the Korean Pharmacopoeia using available botanical databases, they failed to show how nomenclatural concepts are applicable due to misconception of legitimacy and the confusion about synonym. From a nomenclatural perspective, 'accepted name' or 'recommended name' is a subjective term which used to be employed for convenience in a certain databases or working group without nomenclatural meaning. Doh and Lee also pointed out the standardization of author citation. However, they missed the importance of author citation error such as basionym or validating authors. Conclusions : Doh and Lee were not able to solve nomenclatural problems of the Korea Pharmacopoeia due to lack of clarity on the nomenclature code. We strongly recommend that KFDA has to commence extensive nomenclatural review for the next revision of Korea Pharmacopoeia.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.662-666
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• 2006

Korea Institute of Science and Technology Information (KISTI) is the national information center that has collected, analyzed and managed Science and Technology information resources comprehensively since 1962. KISTI has conducted several researches and made "KISTI Information Resources Development Policy" in order to select overseas S&T core journals. This paper aims to develop overseas S&T core journals that have not distributed in the country as the part of this effort. This paper analyses the documents requested to KISTI and delivered from overseas information centers for the last 6 years. This paper selects overseas S&T core journals based on the domestic request through conducting journal usage analysis, citation analysis, and cost effectiveness analysis.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2022.09a
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• pp.48-48
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• 2022

Ethnobotany is an interdisciplinary science at the intersection of botany and ethnology. Currently, there is a sharply increasing need for the study and conservation of traditional knowledge about plants. The loss of traditional sources, knowledge, and practices in using plants is caused by the growth of technologies in all branches of production, widespread urbanization, and globalization of the economy. This study was been conducted to collect and analyze the Koryoins (Koryo saram) traditional ethnobotanical knowledge, living in Uzbekistan, whose number 174,200 people. They are the descendants of Korean immigrants to the Russian Far East, who ended up in Central Asia as a result of the forced resettlement in 1937. In the processing of collected data, four main categories of uses were defined - Alimentary, Medicinal, Household/Handicraft, and Others. For quantitative data analysis, synthetic indices were used - RFC (Relative Frequency of Citation) and CI (Cultural Importance Index), which are commonly applied to assess the importance of plants. The respondents mentioned 72 plants belonging to 28 botanical families. A significant part of them was cultivar plants. The category that had the largest number of plants mentioned by the respondents was the Alimentary use category (51). According to quantitative indices rates, the most important plants are traditionally used for food. A comparison of ethnobotanical knowledge was made with the collected data of this study and Korean traditional knowledge.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.