• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.183-190
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• 2012

This study constructs an expert search system which has a mutual cooperation function based on thesis and author profile. The proposed methodology is as follows. First, we propose weighting method which can search a keyword and the most relevant keyword. Second, we propose a method which can search the experts efficiently with this weighting method. On the preferential basis, keywords and author profiles are extracted from the papers, and experts can be searched through this method. This system will be available to many fields of social network. However, this information is distributed to many systems. We propose a method using multi-ontology to integrate distributed data. The multi-ontology is composed of meta ontology, instance ontology, location ontology and association ontology. The association ontology is constructed through analysis of keyword association dynamically. An expert network is constructed using this multi-ontology, and this expert network can search expert through association trace of keyword. The expert network can check the detail area of expertise through the research list which is provided by the system.

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

• Journal of Korean Elementary Science Education
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• v.37 no.1
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• pp.27-38
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• 2018

The purpose of this study was to investigate the perceptions of the students about professionalism on the elementary teachers' science teaching through network analysis of keyword. For this study, questionnaires were conducted to elementary school students. The collected data were translated by coding and data cleaning. And then analyzed by Gephi 0.9.2 program as a tool of the network analysis. The results of this study were as follows: Top 5 words in betweenness centrality were 'smart, experiment, fun, various, and student understanding'. There was some difference of perceptions of students according to personal backgrounds(gender, grade and interest toward science class). Based on the result of this study, implications to improve elementary teachers' science teaching professionalism were suggested.

• Korean Medical Education Review
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• v.23 no.3
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• pp.176-184
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• 2021

The aim of this study was to analyze the research trends in articles published in the Korean Journal of Medical Education (KJME) and Korean Medical Education Review (KMER) using keyword network analysis. The analyses included 507 papers from 2010 to 2019 published in KJME and KMER. First, keyword frequency analysis showed that the research topics that appeared in both journals were "medical student," "curriculum," "clinical clerkship," and "undergraduate medical education." Second, centrality analysis of a network map of the keywords identified "curriculum" and "medical student" as highly important research topics in both journals. Third, a cluster analysis of 20 core keywords in KMER identified research clusters related to academic motivation, achievement, educational measurement, medical competence, and clinical practice (centered on "learning," while in KJME, clusters were related to educational method and program evaluation, medical competence, and clinical practice (centered on "teaching"). In conclusion, future medical education research needs to expand to encompass other research areas, such as educational methods, student evaluations, the educational environment, student counseling, and curriculum.

• Informatization Policy
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• v.27 no.4
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• pp.24-46
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• 2020

This study proposes a way to timely forecast open government data (OGD) demand(i.e., OGD requests, search queries, etc.) by using keyword network analysis. According to the analysis results, most of the OGD belonging to the high-demand topics are provided by the domestic OGD portal(data.go.kr), while the OGD related to users' actual needs predicted through topic association analysis are rarely provided. This is because, when providing(or selecting) OGD, relevance to OGD topics takes precedence over relevance to users' OGD requests. The proposed keyword network analysis framework is expected to contribute to the establishment of OGD policies for public institutions in the future as it can quickly and easily forecast users' demand based on actual OGD requests.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.43-49
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• 1996

This research proposes a system for speaker independent Korean continuous speech recognition with 247 DDD area names using keyword spotting technique. The applied recognition algorithm is the Dynamic Programming Neural Network(DPNN) based on the integration of DP and multi-layer perceptron as model that solves time axis distortion and spectral pattern variation in the speech. To improve performance, we classify word model into keyword model and non-keyword model. We make an experiment on postprocessing procedure for the evaluation of system performance. Experiment results are as follows. The recognition rate of the isolated word is 93.45% in speaker dependent case. The recognition rate of the isolated word is 84.05% in speaker independent case. The recognition rate of simple dialogic sentence in keyword spotting experiment is 77.34% as speaker dependent, and 70.63% as speaker independent.

• Journal of Society of Preventive Korean Medicine
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• v.25 no.2
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• pp.33-43
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• 2021

Objectives : This study seeks to grasp the current status of Koryo medical research in North Korea, by focusing on researchers and research topics. Methods : A network analysis of co-authors and keyword which were extracted from Koryo Medicine - a North Korean traditional medicine journal, was conducted. Results : The results of author network analysis was a sparse network due to the low correlation between authors. The domain-wide network density of co-authors was 0.001, with a diameter of 14, average distance between nodes 4.029, and average binding coefficient 0.029. The results of the keyword network analysis showed the keyword "traditional medicine" had the strongest correlation weight of 228. Other keywords with high correlation weight was common acupuncture (84) and intradermal acupuncture(80). Conclusions : Although the co-authors of the Koryo Medicine did not have a high correlation with each other, they were able to identify key researchers considered important for each major sub-network. In addition, the keywords of the Koryo Medicine journals had a very high linkage to herbal medicines.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.511-518
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• 2012

In this study, the trend in green technology was observed and the domain of the green technology area that will be actively studied in the future was found by establishing knowledge map in green technology area and comparing and analyzing green technology information in Korea and overseas in time series. For the purpose of this study, network analysis was conducted for the keyword of green technology information provided by green technology information portal site (www.gtnet.go.kr) operated by Korea Institute of Science and Technology Information. Network analysis was conducted using keyword, and change of study subject was found by dividing the analysis result into periods. In the result of network analysis on top 100 keywords from total English keyword, it was found that renewable energy related areas such as solar energy and biomass had high centrality. When the main keyword trend by year was studied, centrality of solar cell, nanotechnology, smart grid, and fuel cell were found to increase, showing that research and development in generation and use of renewable energy are actively made.

• Journal of Korean Library and Information Science Society
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• v.47 no.3
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• pp.95-114
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• 2016

The purpose of this preliminary study is to collect specific examples of book reports and understand semantic characteristics of them through semantic network. The analysis was conducted with 23 book reports which classified by three groups. The keywords were selected from the of book reports. Five types of keyword network were composed based on co-occurrence relations with keywords. The result of this study is following these. First, each keyword network of book reports of groups and individuals is shown to have different structural characteristics. Second, each network has different high centrality keywords according to the result analysis of 3 types of centrality(degree centrality, closeness centrality, betweenness centrality). These characteristic means that keyword network analysis is useful in recognizing the characteristics of not only groups' and but also individual's book reports.