• Title/Summary/Keyword: Keyword Analysis

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Understanding of Structural Changes of Keyword Networks in the Computer Engineering Field (컴퓨터공학 분야 키워드네트워크의 구조적 변화 이해)

  • Kwon, Yung-Keun
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.3
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    • pp.187-194
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    • 2013
  • Recently, there have been many trials to analyze characteristics of research trends through a structural analysis of keyword networks in various fields. However, most previous studies have mainly focused on structural analysis harbored in some static networks and there is a lack of research on changes of such networks structure with time. In this paper, we constructed annual keyword networks by using a database of papers published in the international computer engineering-field journals from 2002 through 2011, and examined the changes of them. As a result, it was shown that most keywords in a network are preserved in the network of the next year, and their degree of connectivity and the average weight of the connections were higher and smaller, respectively, than those of the keywords which are not preserved. In addition, when a keyword network shifted to one of the next year, the connections between keywords were more likely to be removed than preserved, and the average weight of the removal connections was higher than that of the preserved ones. These results imply that the keywords are not changed over time but their connections are very likely to be changed; and there is apparent differences between the preserved and removal groups of keywords/connections with respect to degree and weights of connections. All these results are consistently observed over the ten-year datasets and they can be important principles in understanding the structural changes of the keyword networks.

Co-author and Keyword Networks and their Clustering Appearance in Preventive Medicine Fields in Korea: Analysis of Papers in the Journal of Preventive Medicine and Public Health, $1991{\sim}2006$ (국내 예방의학 분야의 공저자.핵심어 네트워크와 군집 양상 - 대한예방의학회지($1991{\sim}2006$) 게재논문의 분석 -)

  • Jung, Min-Soo;Chung, Dong-Jun
    • Journal of Preventive Medicine and Public Health
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    • v.41 no.1
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    • pp.1-9
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    • 2008
  • Objectives : This study evaluated knowledge structure and its effect factor by analysis of co-author and keyword networks in Korea's preventive medicine sector. Methods : The data was extracted from 873 papers listed in the Journal of Preventive Medicine and Public Health, and was transformed into a co-author and keyword matrix where the existence of a 'link' was judged by impact factors calculated by the weight value of the role and rate of author participation. Research achievement was dependent upon the author's status and networking index, as analyzed by neighborhood degree, multidimensional scaling, correspondence analysis, and multiple regression. Results : Co-author networks developed as randomness network in the center of a few high-productivity researchers. In particular, closeness centrality was more developed than degree centrality. Also, power law distribution was discovered in impact factor and research productivity by college affiliation. In multiple regression, the effect of the author's role was significant in both the impact factor calculated by the participatory rate and the number of listed articles. However, the number of listed articles varied by sex. Conclusions : This study shows that the small world phenomenon exists in co-author and keyword networks in a journal, as in citation networks. However, the differentiation of knowledge structure in the field of preventive medicine was relatively restricted by specialization.

Analysis of Structural Characteristics of the Discipline of Public Administration in Korea from the Viewpoint of Research Ecosystem: Focused on Co-author, Citation, and Keyword Network (연구 생태계 관점에서 본 국내 행정학 분야의 구조적 특성 분석 - 공저자, 인용, 키워드 네트워크 중심으로 -)

  • Park, Cho-Hee;Lee, Sung-Sook
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.31 no.1
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    • pp.213-235
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    • 2020
  • This study examined the process of production, utilization and extinction of researches through academic activities to identify the structural characteristics of the field of discipline of administration in Korea from the viewpoint of research ecosystem. To this end, statistical and network analyses were conducted, focusing on bibliographies, references, and keyword for papers published in 29 domestic journals in the field of public administration for the past five years. The results of the analysis, researchers in the field of public administration in Korea maintain a rather horizontal connection and are connected organically rather than separately. In addition, the core academic journals and keyword were extracted to present the connection, and the speed of knowledge transfer and deterioration was measured to identify the phenomenon of decreasing value in literature.

Analysis of Massive Scholarly Keywords using Inverted-Index based Bottom-up Clustering (역인덱스 기반 상향식 군집화 기법을 이용한 대규모 학술 핵심어 분석)

  • Oh, Heung-Seon;Jung, Yuchul
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.11
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    • pp.758-764
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    • 2018
  • Digital documents such as patents, scholarly papers and research reports have author keywords which summarize the topics of documents. Different documents are likely to describe the same topic if they share the same keywords. Document clustering aims at clustering documents to similar topics with an unsupervised learning method. However, it is difficult to apply to a large amount of documents event though the document clustering is utilized to in various data analysis due to computational complexity. In this case, we can cluster and connect massive documents using keywords efficiently. Existing bottom-up hierarchical clustering requires huge computation and time complexity for clustering a large number of keywords. This paper proposes an inverted index based bottom-up clustering for keywords and analyzes the results of clustering with massive keywords extracted from scholarly papers and research reports.

A Keyword Network Analysis on Research Trends in the Area of Health Insurance (건강보험 연구동향에 대한 키워드 네트워크 분석)

  • Lee, Su Jung;Lee, Sun-Hee
    • Health Policy and Management
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    • v.31 no.3
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    • pp.335-343
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    • 2021
  • Background: The purpose of this study was to extract the major areas of interest in health insurance research in Korea, and infer policy agendas related to health insurance by analyzing research keywords. Methods: For this study, 2,590 articles were selected from among 7,459 academic papers related to health insurance published between January 1987 and December 2018, which were looked up using the Research Information Sharing Service (RISS). Keyword extraction and keyword network analysis were performed using the KrKwic, KrTitle, and UCINET software. Results: First, the number of studies in the area of health insurance continued to increase in all government terms, and it was not until after the 2000s that the subjects of health insurance researches were diversified. Second, degree centrality showed that 'medical expenditure' and 'medical utilization' were consistently high-ranking keywords regardless of the government in power. Aging and long-term care insurance-related keywords were ranked higher in the Lee Myung-bak government, Park Geun-hye government, and Moon Jae-in government. Third, betweenness centrality showed the same high ranking in key topics such as medical expenditure and medical utilization, while the ranking of key keywords differed depending on the interests and characteristics of each government policy. Conclusion: We confirm that health insurance as a research topic has been the main theme in Korean health care research fields. Research keywords extracted from articles also corresponded to the main health policies promoted during each government period. Efforts to systematically investigate policy megatrends are needed to plan adaptive future policies.

A Study on Exploring Direction for Future Education for the Common Good Based on Big Data (빅데이터 기반 공동선 증진을 위한 미래교육 방향성 탐색 연구)

  • Kim, Byung-Man;Kim, Jung-In;Lee, Young-Woo;Lee, Kang-Hoon
    • Journal of Convergence for Information Technology
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    • v.12 no.2
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    • pp.37-46
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    • 2022
  • The purpose of this study is to provide basic data onto preparing soft landing plan of future education policy by exploring direction of future education for the common good using big data and keyword network analysis. Based on the big data provided by Textom, data was collected under the keyword 'future education + common Good' and then keyword network analysis was performed. As a result of the research, it was found that 'common good', 'social', 'KAIST future warning', 'measures', 'research', 'future education', 'politics' were common keywords in the social awareness of future education for the common good. The results of this study suggest that the social awareness of future education for the common good is related to factors related to human, physical environment, social response, academic interest, education policy, education plan, and related variables, It was closely related. Based on these results, we suggested implications for the support for the preparation of a soft landing plan of future education for the common good.

Trends in Domestic Research on Knowledge Management by Using Keyword Analysis (키워드 분석을 통한 지식경영 관련 국내연구 동향)

  • Kim, Bum Seok;Lee, Sungtaek
    • Knowledge Management Research
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    • v.24 no.4
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    • pp.1-22
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    • 2023
  • Knowledge management can be defined as the valuable storing and creation of new knowledge, as well as the sharing of this knowledge to be applied in all areas of an organization's management activities (Turban et al., 2003). In a knowledge-based society where intangible intellectual assets are the source of competitive advantage rather than tangible assets, knowledge management activities are emphasized in both academia and industry. This study analyzes research on "knowledge management" keyword indexed in the Korean Citation Index (https://kci.go.kr), operated by the National Research Foundation of Korea (NRF), to identify related research trends in Korea and suggest future directions for knowledge management activities. The results show that knowledge management is being researched through the integration of various fields and theories, indicating the potential for expanding research topics and fostering interdisciplinary collaboration. Furthermore, the study of knowledge management often include the keyword 'innovation', emphasizing its significant role in organizational and technological innovations. The analysis of keywords by year also reveals that they reflect the major environmental changes of each period, demonstrating the increasing importance of knowledge management in the era of the Fourth Industrial Revolution.

S&T Policy Trend Using Language Network Analysis: Focusing on Science and Technology Basic Plan

  • Kim, Yun Jong;Jeong, Dae-hyun;Oh, Hyunchul
    • Asian Journal of Innovation and Policy
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    • v.6 no.2
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    • pp.111-137
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    • 2017
  • This study analyzes a language network of Science and Technology Basic Plan, which is the basis for science and technology policy in Korea, for the next Science and Technology Basic Plan. Language network analysis was adopted for a quantitative approach measuring the trend of policies. Several techniques such as keyword analysis, language network map analysis, quantitative characteristics analysis and keyword-related major-word analysis have been performed. Results show that there are common policies emphasized by all Science and Technology Basic Plans in the past, and there are also specific policies emphasized in each period of the Science and Technology Basic Plan. These specific policies come from a 'change of times' when the Science and Technology Basic Plans were established, as well as the philosophy of the national government.

An Empirical Study on Statistical Optimization Model for the Portfolio Construction of Sponsored Search Advertising(SSA) (키워드검색광고 포트폴리오 구성을 위한 통계적 최적화 모델에 대한 실증분석)

  • Yang, Hognkyu;Hong, Juneseok;Kim, Wooju
    • Journal of Intelligence and Information Systems
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    • v.25 no.2
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    • pp.167-194
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    • 2019
  • This research starts from the four basic concepts of incentive incompatibility, limited information, myopia and decision variable which are confronted when making decisions in keyword bidding. In order to make these concept concrete, four framework approaches are designed as follows; Strategic approach for the incentive incompatibility, Statistical approach for the limited information, Alternative optimization for myopia, and New model approach for decision variable. The purpose of this research is to propose the statistical optimization model in constructing the portfolio of Sponsored Search Advertising (SSA) in the Sponsor's perspective through empirical tests which can be used in portfolio decision making. Previous research up to date formulates the CTR estimation model using CPC, Rank, Impression, CVR, etc., individually or collectively as the independent variables. However, many of the variables are not controllable in keyword bidding. Only CPC and Rank can be used as decision variables in the bidding system. Classical SSA model is designed on the basic assumption that the CPC is the decision variable and CTR is the response variable. However, this classical model has so many huddles in the estimation of CTR. The main problem is the uncertainty between CPC and Rank. In keyword bid, CPC is continuously fluctuating even at the same Rank. This uncertainty usually raises questions about the credibility of CTR, along with the practical management problems. Sponsors make decisions in keyword bids under the limited information, and the strategic portfolio approach based on statistical models is necessary. In order to solve the problem in Classical SSA model, the New SSA model frame is designed on the basic assumption that Rank is the decision variable. Rank is proposed as the best decision variable in predicting the CTR in many papers. Further, most of the search engine platforms provide the options and algorithms to make it possible to bid with Rank. Sponsors can participate in the keyword bidding with Rank. Therefore, this paper tries to test the validity of this new SSA model and the applicability to construct the optimal portfolio in keyword bidding. Research process is as follows; In order to perform the optimization analysis in constructing the keyword portfolio under the New SSA model, this study proposes the criteria for categorizing the keywords, selects the representing keywords for each category, shows the non-linearity relationship, screens the scenarios for CTR and CPC estimation, selects the best fit model through Goodness-of-Fit (GOF) test, formulates the optimization models, confirms the Spillover effects, and suggests the modified optimization model reflecting Spillover and some strategic recommendations. Tests of Optimization models using these CTR/CPC estimation models are empirically performed with the objective functions of (1) maximizing CTR (CTR optimization model) and of (2) maximizing expected profit reflecting CVR (namely, CVR optimization model). Both of the CTR and CVR optimization test result show that the suggested SSA model confirms the significant improvements and this model is valid in constructing the keyword portfolio using the CTR/CPC estimation models suggested in this study. However, one critical problem is found in the CVR optimization model. Important keywords are excluded from the keyword portfolio due to the myopia of the immediate low profit at present. In order to solve this problem, Markov Chain analysis is carried out and the concept of Core Transit Keyword (CTK) and Expected Opportunity Profit (EOP) are introduced. The Revised CVR Optimization model is proposed and is tested and shows validity in constructing the portfolio. Strategic guidelines and insights are as follows; Brand keywords are usually dominant in almost every aspects of CTR, CVR, the expected profit, etc. Now, it is found that the Generic keywords are the CTK and have the spillover potentials which might increase consumers awareness and lead them to Brand keyword. That's why the Generic keyword should be focused in the keyword bidding. The contribution of the thesis is to propose the novel SSA model based on Rank as decision variable, to propose to manage the keyword portfolio by categories according to the characteristics of keywords, to propose the statistical modelling and managing based on the Rank in constructing the keyword portfolio, and to perform empirical tests and propose a new strategic guidelines to focus on the CTK and to propose the modified CVR optimization objective function reflecting the spillover effect in stead of the previous expected profit models.

Phrase-based Indexing for Korean Information Retrieval System (한국어 정보검색 시스템을 위한 구 단위 색인)

  • 윤성희
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.5 no.1
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    • pp.44-48
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    • 2004
  • This paper proposes a phrase-based indexing system based on the phrase. the larger syntax unit than a single keyword. Early information retrieval systems with indexing system matching single keyword is simple and popular. But with single keyword matching it is very hard to represent the exact meaning of documents and the set of documents from retrieval is very large, therefore it can't satisfy the user of the information retrieval systems. Web documents include lots of syntactic errors, the natural language parser with high quality cannot be expected in Web. Partial trees, even not a full tree, from fully bottom-up parsing is still useful for extracting phrases, and they are much more discriminative than single keyword for index. It helps the information retrieval system enhance the efficiency and reduce the processing overhead, too.

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