• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.41-63
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• 2024

This study investigated research trends in digital curation indexed in a prominent domestic academic information database. A systematic literature review was conducted on 39 academic papers published from 2009 to 2023. The review examined indexing status according to publication year, venue, academic discipline, research area distribution, research affiliation and occupation, and research types. In addition, network centrality analysis and cohesive group analysis were performed on 69 author keywords. The findings revealed several key points. First, digital curation research peaked in 2015 and 2016 with 5 publications each year, followed by a slight decrease, and then consistently produced 4 or more publications annually since 2019. Second, among the 39 studies, 25 were conducted in interdisciplinary fields, including library and information science, while 11 were in the humanities, such as miscellaneous humanities. The most prominent research areas were theoretical and infrastructural aspects, information management and services, and institutional domains. Third, digital curation research was predominantly led by university-affiliated professors and researchers, with collaborative research more prevalent than solo research. Lastly, analysis of author keywords revealed that "digital curation," "institution," and "content" were the most influential central keywords within the overall network.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• Journal of Physiology & Pathology in Korean Medicine
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• v.38 no.1
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• pp.1-9
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• 2024

Cognitive reserve (CR) is a concept that can explain the discrepancies between the pathologic burden of the disease and clinical manifestations. It refers to the individual susceptibility to age-related brain changes and pathologies related to Alzheimer's disease, thus recognized as a factor affecting the trajectories of the disease. The purpose of this study was to explore the current states of clinical studies on neural substrates of CR in Alzheimer's disease using functional magnetic resonance imaging. We searched for clinical studies on CR using fMRI in the Pubmed, Cochrane library, RISS, KISS and ScienceON on August 14, 2023. Once the online search was finished, studies were selected manually by the inclusion criteria. Finally, we analyzed the characteristics of selected articles and reviewed the neural substrates of CR. Total thirty-four studies were included in this study. As surrogate markers of CR, not only education and occupational complexity, but also composite score and questionnaire-based method, which cover various areas of life, were mainly used. The most utilized methods in resting-state fMRI were independent component analysis, seed-based analysis, and graph theory analysis. Through the analysis, we demonstrated that neuroimaging techniques could capture the neural substrates associated with cognitive reserve. Moreover, functional connectivity of brain regions centered on prefrontal and parietal cortex and network areas such as default mode network showed a significant correlation with CR, which indicated a significant association with cognitive performance. CR may induce differential effects according to the disease status. We hope that this perspective on cognitive reserve would be helpful when conducting clinical researches on the mechanisms of traditional Korean medicine for Alzheimer's disease in the future.

• Journal of the Korean Society for information Management
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• v.41 no.2
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• pp.295-316
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• 2024

Analyzing research trends is essential for the sustainable development of a discipline and is important for understanding the value of prior research and laying the groundwork for subsequent research. This study aims to draw implications for the future direction of convergence research on the disclosure of information from various disciplines by comparing and analyzing the trends in disclosure of information research in Korea. For this purpose, we analyzed the publication frequency of information disclosure papers listed in the Korea Citation Index (KCI) from 2002 to 2023 and the publication trend by discipline as a time series. In addition, we compared the keyword relationships and specialized research topics of each discipline by applying network analysis and LDA topic modeling techniques to the names and keywords of papers in law, public administration, and library and information science. As a result of the analysis, the law focuses on legal regulations and policy improvement, public administration focuses on changing social needs and administrative operation methods, and LIS focuses on practical approaches to record management and disclosure of information. Based on this, future research directions include combining policy research in law with social change research in public administration and developing realistic policies and operational guidelines from the practical perspective of LIS. Such convergent research will enable the systematic and efficient implementation of disclosure of information systems, contributing to the guarantee of the public's right to know and the enhancement of state transparency.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.53-77
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• 2012

This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na$\ddot{i}$ve Bayesian classifier algorithm provided in the MALLET package was used to classify all the paragraphs in articles. After classification, Precision, Recall and F-value were used to evaluate the results of classification. Based on the results of this study, three subjects such as Culture, Eco-business and Politics showed some differences in contents and tones of arguments among these three newspapers. In addition, for the National issues, tones of arguments on 4-major-rivers project were different from each other. It seems three newspapers have their own specific tone of argument in those sections. And keyword-networks showed different shapes with each other in the same period in the same section. It means that frequently appeared keywords in articles are different and their contents are comprised with different keywords. And the Positive-Negative classification showed the possibility of classifying newspapers' tones of arguments compared to others. These results indicate that the approach in this study is promising to be extended as a new tool to identify the different tones of arguments of newspapers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.599-610
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• 2016

This paper reports the results of a systematic review (SR) and meta-analysis research to compare the effect of Kangaroo care, targeting mothers and premature infants. A randomized clinical trial study was performed until February 2015. The domestic literature contained the non-randomized clinical trial research without restriction according to the level of the study design. A search of the Ovid-Medline, CINAHL, PubMed and KoreaMed, the National Library of KOREA, the National Assembly Library, NDSL, KISS and RISS. Through the KMbase we searched and combined the main term ((kangaroo OR KC OR skin-to-skin) AND (care OR contact)) AND (infant OR preterm OR Low Birth Weight OR LBW), ((kangaroo OR kangaroo OR kangaroo) AND (care OR nursing care OR management OR skin contact)) was made; these were all combined with a keywords search through the selection process. They were excluded in the final 25 studies (n=3051). A methodology checklist for randomized controlled trials (RCTs) designed by SIGN (Scottish Intercollegiate Guidelines Network) was utilized to assess the risk of bias. The overall risk of bias was regarded as low. In 16 studies that were evaluated as a grade of "++", 9 studies were evaluated as a grade of "+". As a result of meta-analysis, kangaroo care regarding the effects of premature mortality, severe infection/sepsis had an insignificant effect. Hyperthermia incidence, growth and development (height and weight), mother-infant attachment, hypothermia incidence, length of hospital days, breast feeding rate, sleeping, anxiety, confidence, and gratification of mothering role were considered significant. In satisfaction of the role performance, depression and stress presented contradictory research results for individual studies showing overall significant difference. This study has some limitations due to the few RCTs comparing kangaroo care in the country. Therefore, further RCTs comparing kangaroo care should be conducted.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6099-6111
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• 2015

This study was a systematic review and meta-analysis comparing the effects of aquatic exercise and land-based exercise in the treatment of knee osteoarthritis. 7 studies (n=449) met selection and exclusion criteria out of 287 potential studies obtained from the literature search via Ovid-Medline, Cochrane Library CENTRAL, CINAHL, RISS and KISS. The overall risk of bias of selected studies using SIGN (Scottish Intercollegiate Guidelines Network) checklist for randomized controlled trials (RCT) was regarded as low. As a result of meta analysis, Standardized Mean Difference (SMD) for pain was -0.26(95% CI -0.49, -0.03, p=0.03, $I^2=14%$), which implies that aquatic exercise groups had significant less pain than land-based exercise groups. On the other hand, there was no significant difference between aquatic exercise groups and land based exercise groups for flexion Range of Motion (ROM) (-0.12, 95% CI -0.51, 0.27, p=0.53, $I^2=0%$), extension ROM (-0.04, 95% CI -0.55, 0.48, p=0.89, $I^2=43%$), physical function (-0.12, 95% CI -0.44, 0.19, p=0.44, $I^2=0%$), Quality of Life (QOL) (-0.15, 95% CI -0.54, 0.24, p=0.46, $I^2=0%$). This study has some limitations due to few RCTs comparing aquatic exercise groups and land-based exercise groups in the treatment of knee osteoarthritis. Therefore, further RCTs should be conducted along with long-term outcomes.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.