• The Journal of Korean Association of Computer Education
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• v.6 no.1
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• pp.9-17
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• 2003

XML(eXtensible Markup Language), next generation internet standard language has the advantage of easy re-use and re-structure in other computing environment because it has the separate data, presentation and structure. In this paper, we implement the efficient retrieval system for the general user by limiting the XML documents on the multimedia learning contents for the virtual education system. The system design is based on SCO Metadata unit defined in SCORM as the proposed virtual education standard. Each XML documents has three indexes - keyword, element and attribute. Also, it makes possible to retrieve data without previous knowledge of the DTD by making the element retrieval screen structure for the user interface. And it gives the user various result screen formats such as XML and HTML by restructuring the retrieval result through XML-QL and XSL, respectively.

• 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 Fisheries and Marine Sciences Education
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• v.29 no.1
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• pp.224-233
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• 2017

This study aims to analyze the research trend of lifelong education in Korea over the last 10 years based on social network analysis. To do this, a dataset has been collected from KCI (Korea Citation Index) database. According to the results of the study, firstly, the current status of lifelong education research by the year in the last 10 years showed a relatively high ratio between 2008 and 2009 and 2014 ~ 2015. Secondly, the most active networks between authors and journals constitute a key group in the order of 'Lifelong Education Study' and 'Lifelong Learning Society'. Thirdly, the research institutes with the largest number of lifelong education research papers are Soongsil University, Dong-Eui University, and Korea National Open University. In the network with the authors' network, the only authors were K8 working at Chonbuk National University, and the co-authors, H4, who works at Kyungpook National University, showed the most active network. Finally, the core keyword network based on the thesis topic was analyzed as having higher connection centrality in the order of 'lifelong education', 'lifelong educator', and 'university lifelong education'.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.13-20
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• 2006

In this paper, we constructed a spam-mail filtering system based on the lexical and conceptual information. There are two kinds of information that can distinguish the spam mail from the legitimate mil. The definite information is the mail sender's information, URL, a certain spam keyword list, and the less definite information is the word lists and concept codes extracted from the mail body. We first classified the spam mail by using the definite information, and then used the less definite information. We used the lexical information and concept codes contained in the email body for SVM learning. According to our results the spam precision was increased if more lexical information was used as features, and the spam recall was increased when the concept codes were included in features as well.

• The Journal of Information Systems
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• v.30 no.3
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• pp.201-216
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• 2021

Purpose The purpose of this study is to analyze the trend of patent technology in textile materials using text mining methodology based on Dynamic Embedded Topic Model and Structural Topic Model. It is expected that this study will have positive impact on revitalizing and developing textile materials industry as finding out technology trends. Design/methodology/approach The data used in this study is 866 domestic patent text data in textile material from 1974 to 2020. In order to analyze technology trends from various aspect, Dynamic Embedded Topic Model and Structural Topic Model mechanism were used. The word embedding technique used in DETM is the GloVe technique. For Stable learning of topic modeling, amortized variational inference was performed based on the Recurrent Neural Network. Findings As a result of this analysis, it was found that 'manufacture' topics had the largest share among the six topics. Keyword trend analysis found the fact that natural and nanotechnology have recently been attracting attention. The metadata analysis results showed that manufacture technologies could have a high probability of patent registration in entire time series, but the analysis results in recent years showed that the trend of elasticity and safety technology is increasing.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.93-100
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• 2022

Keyword-oriented search methods are mainly used as data search methods, but this is not suitable as a search method in the legal field where professional terms are widely used. In response, this paper proposes an effective data search method in the legal field. We describe embedding methods optimized for determining similarities between sentences in the field of natural language processing of legal domains. After embedding legal sentences based on keywords using TF-IDF or semantic embedding using Universal Sentence Encoder, we propose an optimal way to search for data by combining BERT models to check similarities between sentences in the legal field.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.77-89
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• 2022

Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.

• Journal of the Korean School Mathematics Society
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• v.15 no.1
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• pp.81-108
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• 2012

One of the keyword in every nation's recent educational policy is key competencies. Considering national competitiveness originating from educational competitiveness, educational policy has been driven to identify key competencies and realize them through school education. Within this context some countries have developed competencies-based curriculum and discussed ways to relate key competencies and subject matter areas. However, there have been few researches on how to reflect or integrate key competencies into subject matter areas. Because of this reason, the ways to incorporate and integrate key competencies into three subject areas including mathematics were investigated. The recent trends of curriculum, teaching and learning, and assessment of domestic and foreign cases were explored by the subject of one Korean international middle school, one British foreign school in Seoul, one French foreign school in Seoul, and four middle schools in New Zealand. To establish competencies-based school education, there should be intimate connection system among curriculum, teaching and learning, assessment, and teacher education. Through analysis of domestic and foreign cases, some conclusions regarding how these aspects have changed with the emphasis of key competencies were drawn. In this paper, through classroom observation and teacher interview, a case of the competencies-based mathematics lessons of one French foreign school was investigated. As a result, summaries and recommendations related to ways to improve subject teaching and teacher education in light of key competencies were presented. In these recommendations, the ways to reconstruct subject-based curriculum, the content-specific teaching and learning, and educational assessment were included.

• Journal of the Korean School Mathematics Society
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• v.15 no.2
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• pp.215-238
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• 2012

One of the keyword in every nation's recent educational policy is key competencies. Considering national competitiveness originating from educational competitiveness, educational policy has been driven to identify key competencies and realize them through school education. Within this context some leading countries have developed competencies-based curriculum and discussed ways to relate key competencies and subject matter areas. However, there have been few researches on how to reflect or integrate key competencies into subject matter areas. Because of this reason, the ways to incorporate and integrate key competencies into three subject areas including mathematics were investigated. The recent trends of curriculum, teaching and learning, and assessment of domestic and foreign cases were explored by the subject of one Korean international middle school, one British foreign school in Seoul, one French foreign school in Seoul, and four middle schools in New Zealand. To establish competencies-based school education, there should be intimate connection system among curriculum, teaching and learning, assessment, and teacher education. Through analysis of domestic and foreign cases, some conclusions regarding how these aspects have changed with the emphasis of key competencies were drawn. In this paper, through classroom observations and teacher interviews, the reality of competencies-based mathematics teaching of New Zealand and France was investigated. As a result, summaries and recommendations related to ways to improve subject teaching and teacher education in light of key competencies were presented. In these recommendations, the ways to reconstruct subject-based curriculum, the content-specific teaching and learning, and educational assessment were included.

• Journal of Internet Computing and Services
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• v.16 no.4
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• pp.39-50
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• 2015

In information retrieval process through search engine, some users want to retrieve several documents that are corresponding with specific time period situation. For example, if user wants to search a document that contains the situation before 'Japanese invasions of Korea era', he may use the keyword 'Japanese invasions of Korea' by using searching query. Then, search engine gives all of documents about 'Japanese invasions of Korea' disregarding time period in order. It makes user to do an additional work. In addition, a large percentage of cases which is related to historical documents have different time period between generation date of a document and record time of contents. If time period in document contents can be extracted, it may facilitate effective information for retrieval and various applications. Consequently, we pursue a research extracting time period of Joseon era's historical documents by using historic literature for Joseon era in order to deduct the time period corresponding with document content in this paper. We define historical objects based on historic literature that was collected from web and confirm a possibility of extracting time period of web document by machine learning techniques. In addition to the machine learning techniques, we propose and apply the similarity filtering based on the comparison between the historical objects. Finally, we'll evaluate the result of temporal indexing accuracy and improvement.