• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Proceedings of the Technology Innovation Conference
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• 2003.06a
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• pp.3-11
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• 2003

As the size and scope of R&D investment explodes, the strategic and managerial importance of R&D proposal screening becomes highlighted. This point is particularly true for a large-scale research center that deals with multi-product and multi-technology R&D projects. Despite the importance, however, previous research has focused on project evaluation and selection stage. In this research, we propose a R&D proposal screening system. The main objective of the system is to filter R&D proposals that are identified to be duplications of past or existing projects. To this end, the algorithm of the system employs text mining, multivariate statistical method, and case-based reasoning.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.75-88
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• 2023

In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.614-618
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• 2008

As the multimedia data increase a lot with the rapid development of the Internet, an efficient retrieval technique focusing on individual users is required based on the analyses of such data. However, user modeling services provided by recent web sites have the limitation of text-based page configurations and recommendation retrieval. In this paper, we construct the user preference model with a Bayesian network to apply the user modeling to video retrieval, and suggest a method which utilizes probability reasoning. To do this, context information is defined in a real office environment and the video scripts acquired from established cameras and annotated the context information manually are used. Personal information of the user, obtained from user input, is adopted for the evidence value of the constructed Bayesian Network, and user preference is inferred. The probability value, which is produced from the result of Bayesian Network reasoning, is used for retrieval, making the system return the retrieval result suitable for each user's preference. The usability test indicates that the satisfaction level of the selected results based on the proposed model is higher than general retrieval method.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.30-43
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• 2022

In this study, elementary students' science communication competence was investigated based on the grammatical features expressed in their language-use in classroom discourse and science writings. The classes were designed to integrate the evidence-based reasoning framework and traditional learning cycle and were conducted on fifth graders in an elementary school. Eight elementary students' discourse data and writings were analyzed using lexico-grammatical resource analysis, which examined the discourse text's content and logical relations. The results revealed that the student language used in analyzing data, interpreting evidence, or constructing explanations did not precisely conform to the grammatical features in science language use. However, they provided examples of grammatical metaphors by nominalizing observed events in the classroom discourses and those of causal relations in their writings. Thus, elementary students can use science language grammatically from science language-use experiences through listening to a teacher's instructional discourses or recognizing the grammatical structures of science texts in workbooks. The opportunities in which elementary students experience the language-use model in science learning need to be offered to understand the appropriate language use in the epistemic context of evidence-based reasoning and learn literacy skills in science.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of National Security and Military Science
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• s.7
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• pp.305-331
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• 2009

This year I published a Korean translation of On War in three volumes, written by Prussian general Carl von Clausewitz. I believe it is the first unabridged Korean translation from the original German text, Vom Kriege. It is true that the work has been translated into Korean several times, but some translations have been done from English or Japanese versions, while others are abridged ones. It is not easy to make a good translation of On War, partly because the book is actually an unfinished work, and partly because it contains almost all academic subjects in social sciences. Moreover, two aspects of the dialectical logic in the book make it more difficult to understand. One is inductive reasoning, the other is deductive explanation. The former is to 'ascend' to draw principles and generalizations from empirical experience, the latter is to 'descend' to describe and explain given principles, often by concrete examples. Considering these difficulties, if we want to have better translations than existing ones, there should be substantial commentaries which contain not only history of wars, but also biographies and geographies concerned. I hope that On War can be taught and studied in many universities, for it will make it easier to produce reliable commentaries.

• English Language & Literature Teaching
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• v.10 no.3
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• pp.89-112
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• 2004

This article examines the ways in which language can be exploited in the manipulation of the reader's interpretation of a text to make him/her take certain lines of thought according to the writer's persuasive intents. Such functions of language provide valid foundations to support the teaching of critical reading skills and to explore an adequate approach to discourse analysis. A pilot study was conducted to find out the extent to which the reader can be coaxed into thinking in some fashions guided by specific linguistic devices employed for ideological texts. Forty-seven subjects divided into two groups (humanities majors and natural science majors at undergraduate level) joined the two-fold questionnaire surveys intended to look at their critical reading abilities. The empirical results indicate that college students whose majors are humanities were more inclined to take a holistic approach in processing commercial advertisement texts and their abilities for critical interpretation appeared to be lower than those of the subjects whose majors are natural sciences, who showed a relatively high tendency to take an analytical approach in decoding the textual facts. As a consequence, pedagogic implications for increasing critical reading abilities have resulted in a set of analytical procedures concerning ideological texts which is linked with instructional guidelines to emphasize the importance of the reader's logical and analytical reasoning power, entirely accepted as a general prerequisite for cracking the covert language gambits.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.457-465
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• 2010

Customized information analysis system is a software system that can help to extract useful information from non-structured natural language data, process the information to customized form, and provide future forecast and reasoning information. To implement the information analysis system, we need natural language processing technology to analyze natural language, information extraction technology to detect necessary entity and its relationship from text, and data mining technology to discover new and unknown information from extracting data. This paper suggest virtual customized information analysis system processing national defense news data and introduce base technologies for information analysis.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.27-44
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• 2019

The goal of this study is to find why the consumers are reacting slowly or negatively toward smartwatches. Even though, smartwatches provide useful information such as health care and text message, the market was not growing fast as expected, and seems to be stagnant at this point. Thus, the future market predictions are varied. To find out why this may have happened, a Q-method study using non-adopters was conducted. In order to find out depth explanations from each interviewers, the research team chose the Q method and Q sorting to classify the different reasons for non-adopters. Based on the interview, all participants were clustered with into groups with similar patterns of the answers. The research team classified the interview group to three categories 1) Technology Discontent 2) Service Discontent 3) Indifferent. The research team analyzed each category reasoning and logics. Also the team compared the result to the technology chasm as it was proposed by Rogers (1969) to measure the maturity of the consumers.