• Journal of Information Technology Applications and Management
• /
• v.19 no.4
• /
• pp.1-18
• /
• 2012

This paper identifies technology trends of national research and development (national R&D) by exploiting Korean national R&D patents, ranging from 2007 to 2010. In this paper, co-word analysis (CWA), which is a method to identify the relationship among technology terms by using their co-occurrences, is incorporated into network analysis to visualize the relationships among technology keywords of national R&D patents and calculate network indexes concerning inter-relationship diversity and strength of technology keywords. As a result, this research found that inter-relationship among technology keywords in national R&D are getting increasingly strengthening in an overall sense. In addition, the keyword inter-relationship diversity-strength map proposed in this paper revealed some significant technological keywords of national R&D : core technology keywords including "sensor", "film" and "fuel" and emerging keywords including "biosensor" and "thermoelectric". Because the proposed approach helps identify interdisciplinary trends of technology keywords from a massive volume of national R&D patents in a visual and quantitative way, we expect that the approach can be incorporated as a preliminary into the R&D planning process to assist R&D policy makers to understand technology convergence of national R&D and develop relevant R&D policies.

• Journal of Information Technology Services
• /
• v.11 no.1
• /
• pp.223-238
• /
• 2012

We have been provided with a plenty of information about IT through the conferences. However, it is hard to find enough information or the latest trends from conferences because there are too many conferences. In this situation, we analyzed the latest trends related to the field of IT by exploiting the Netminer which is one of the software for analysis of social networks and measuring the Cosine Similarity between conferences, based upon keywords which are included in the conferences. We analyzed keywords of 24 conferences related to the computer network part of the IEEE (Institute of Electrical and Electronics Engineers) in the case of foreign conferences. We also analyze keywords of the KIISE (Korean Institute of Information Scientists and Engineers) conferences in the case of domestic conferences, during 2009-2010. We identified the trends through the frequency of keywords, the change of top 10 keywords ranking and the similarity between conferences.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.127-138
• /
• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Asia Marketing Journal
• /
• v.16 no.3
• /
• pp.23-33
• /
• 2014

The selection of keywords for bidding is a critical component of paid search advertising. When the number of possible keywords is enormous, it becomes difficult to choose the best keywords for advertising and then subsequently to assess their effect. To this end, we propose an ultrahigh dimensional keyword selection approach that not only reduces the dimension for selections, but also generates the top listed keywords for profits. An empirical analysis using a unique panel dataset from a large online clothes retailer that advertises on the largest search engine in China (i.e., Baidu) is presented to illustrate the usefulness of our approach.

• Journal of Gifted/Talented Education
• /
• v.26 no.2
• /
• pp.347-363
• /
• 2016

The purpose of this study was to analyze the trends in domestic and international gifted education in the last six years (2010-2015) by utilizing social network analysis methods. For papers of gifted education in Korea, two KCI (Korea Citation Index) rated journals, the 'Gifted/Talented Education' (The Korean Society for the Gifted) and 'Gifted and Talented Education' (The Korean Society for the Gifted and Talented Education) were selected and 457 pieces published in two journals were collected. The papers of 347 published in SSCI rated journals, 'The Gifted Child Quarterly,' 'Journal for the Education of the Gifted,' and 'High Ability Studies' were selected. English keywords were extracted from 457 papers from Korean journals and 347 papers from foreign journals and the Social Network Analysis (SNA) way was utilized for keyword frequency and central network analyses. It was appeared that the trends of paper keywords from domestic and foreign countries showed common keywords, 'academically gifted', 'science gifted', and 'gifted' as center keyword frequency, and keywords, 'achievement', 'identification', 'intelligence' appeared as the most frequent ones. For domestic papers, keywords, 'creativity', 'gifted education', and 'gifted education teacher' were the highest frequent keywords while keywords, 'foreign countries', and 'student attitudes' were most frequent ones for the foreign countries. For the analysis of papers from five journals as one group, it was found that keywords, 'identification', 'intelligence', and 'achievement' were the most important common ones and keywords, 'cognitive', 'motivation', and 'self-concept' were appeared as important keywords. The trend of gifted education in Korea seems to be different from ones of foreign countries, domestic papers of gifted education rarely included keywords of 'foreign examples', 'student attitudes', and 'gender differences.' Consequently, the trend of gifted education in Korea called for various research perspectives.

• Journal of applied mathematics & informatics
• /
• v.10 no.1_2
• /
• pp.333-351
• /
• 2002

This paper introduces a search method based on conceptual graph. A hyperlink information is essential to construct conceptual graph in web. The information is very useful as it provides summary and further linkage to construct conceptual graph that has been provided by human. It also has a property which shows review, relation, hierarchy, generality, and visibility. Using this property, we extracted the keywords of web documents and made up of the conceptual graph among the keywords sampled from web pages. This paper extracts the keywords of web pages using anchor text one out of hyperlink information and makes hyperlink of web pages abstract as the link relation between keywords of each web page. 1 suggest this useful search method providing querying word extension or domain knowledge by conceptual graph of keywords. Domain knowledge was conceptualized knowledged as the conceptual graph. Then it is not listing web documents which is the defect of previous search system. And it gives the index of concept associating with querying word.

• Korean Journal of Oriental Medicine
• /
• v.11 no.2
• /
• pp.35-52
• /
• 2005

This study is to design a Semantic Navigation Retrieval System for Oriental Medicine Articles based on a XTM so that people can search and use them more effectively than before. Keywords extracted from articles are categorized 4 topics : herbs, prescription, disease, and action. Keywords analysis Ontology is modeled based on 4 topics and their relations, and then represented Topic maps. Next, Article analysis Ontology is consist of title, author, keywords, abstracts and organization Topics from metadata. Keywords and Article analysis Ontology were integrated through Keywords Topic. Korean Medical Article Retrieval System is optimistic in terms on search results supporting semantic navigation in the information service aspects and easier accessibility because all related information are semantically connected with each different DBs.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.16 no.5
• /
• pp.622-630
• /
• 2013

The purpose of this study is to investigate trends of characteristics and changes in the field of marine/naval/underwater military science and technology in Korea. This study analyzed the keywords that appeared in marine/naval/underwater section of the "Journal of the Korean Military Science and Technology" during the period, 1998~2012. One hundred and seven articles were analyzed by keywords in English. Four hundred and fifty four keywords in English were analyzed by appearance frequency. Finally those results of source literatures and keywords were compared with each other and a better direction for the future of the field with further studies was suggested.

• Safety and Health at Work
• /
• v.12 no.2
• /
• pp.272-276
• /
• 2021

This brief report aims to establish the keyword content of studies on occupational health and safety-the key framework of the world of work in the trade and health domain. Data were collected from the SCOPUS database, focusing on articles on occupational health and safety and related keywords, with an emphasis on abstracts and titles. Data were analyzed and summarized based on keywords included from the MeSH database. There were 24,499 manuscripts in the domain and 1,346 (5.40%) occupational health-related keywords, including those that overlapped. The most frequently referenced occupational health-related keyword was "occupational health" (452 articles), followed by "occupational safety" (141 articles). There were fewer keywords on occupational health in the trade and health literature. As the world of work has been prioritized because of the recent new normal of work life since the COVID-19 pandemic, examining the focus of occupational health priorities within the global perspective is crucial.

• Journal of information and communication convergence engineering
• /
• v.19 no.1
• /
• pp.48-53
• /
• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.