• Proceedings of the Korea Association of Information Systems Conference
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• 2001.12a
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• pp.285-290
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• 2001

The borderless global economy has accentuated the importance of knowledge as the most critical source of competitive advantage. Thus, knowledge management has become a strategic mandate for most world-class organizations. A key enabler for implementing an effective knowledge management system is advanced information technology. Strategies for developing an enterprise-wide knowledge management system Infrastructure with embedded information technology are discussed. In particular, this paper discusses the concept of a knowledge management life cycle- -knowledge capture, knowledge development, knowledge sharing, and knowledge utilization, and how applications of new information technology support each step of the knowledge management practices within and between organizations is suggested.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• Journal of Distribution Science
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• v.20 no.2
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• pp.79-89
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• 2022

Purpose: This study aims to scrutinize the effect of knowledge sharing on entrepreneurial passion and innovative work behavior. This study also tests the mediating role of entrepreneurial passion on the association between knowledge sharing and innovative work behavior in distribution channel. Research design, data and methodology: A quantitative methodology is adopted to inspect the association between knowledge sharing, entrepreneurial passion, and innovative work behavior. Data are obtained from 193 employees from four stone milling companies in Central Java - Indonesia. The Smart PLS 3.0 software is used to verify and test the offered hypotheses. Results: The significant empirical findings reveal that knowledge sharing positively affects entrepreneurial passion and innovative work behavior. Also, entrepreneurial passion positively affects innovative work behavior. In addition, this study brings to the light that entrepreneurial passion mediates the association between knowledge sharing and innovative work behavior. These results suggest that organizations should freely facilitate knowledge-sharing behavior to increase entrepreneurial passion within the organization, thereby promoting innovative work behavior. Conclusions: This study presents a significant contribution to the development of knowledge in business because the studies on the association between knowledge sharing and innovative work behavior have not taken into account the mediating role of entrepreneurial passion.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• Journal of the Korean Home Economics Association
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• v.47 no.5
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• pp.35-48
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• 2009

Parenting knowledge is regarded as an important variable influencing parenting style. This study investigated the parenting knowledge of mothers with infants and analyzed how this knowledge influenced their parenting style. The mothers’ parenting efficacy was included in the analysis as a mediator between parenting knowledge and parenting style. Three hundred and seventy-five mothers with infants completed questionnaires regarding parenting knowledge, parenting efficacy, and parenting style. The results showed that the mothers reported different scores in subcategories of parenting knowledge, in which the highest scores were in knowledge about rearing behavior and the lowest were in the developmental process. Second, there were differences in parenting knowledge scores according to the age, employment status, and educational level of subjects. Third, subjects with the more accurate parenting knowledge reported more positive parenting efficacy and parenting style. Lastly, parenting efficacy completely mediated between parenting knowledge and parenting style.

• Journal of the Korean Home Economics Association
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• v.44 no.5 s.219
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• pp.59-68
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• 2006

This study investigated preschoolers' emotionality, emotional knowledge, event knowledge, and interpersonal problem solving strategies according to their sex and age, and the relationships among them. Subjects were 116 preschoolers (73 boys and 43 girls; 68 four- and 48 five-year-olds). Results showed that girls were higher in negative emotionality (sadness) than boys. Also, 5-year-old children were higher in emotional knowledge, event knowledge, and forceful problem solving strategies than 4-year-olds. Furthermore, children's event knowledge was positively related to their relevant problem solving strategies, while children's event knowledge was negatively related to their forceful problem solving strategies. These findings provide a preliminary evidence that children's event knowledge may predict their interpersonal problem solving strategies.

• Journal of the Korean Dietetic Association
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• v.29 no.1
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• pp.49-64
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• 2023

Osteoporosis is a major health problem confronting middle-aged women today. Enhancing calcium intake in early adulthood can increase the rate of calcium gain in bone. In this study, we investigated the association of bone health-related nutritional knowledge levels with calcium-related dietary behavior and nutrition education among women. Data were collected using questionnaires from 347 women aged 20~30 residing in Gyeonggi-do. Subjects were categorized into two groups according to their bone health-related nutritional knowledge (high or low-knowledge group). Knowledge related to bone health and calcium, and dietary habits was assessed, and the preference for and intake frequency of calcium-rich food were collected and analyzed using food frequency questionnaires. The high-knowledge group showed a significantly higher rate of nutritional education experience (33.9%) when compared with the low-knowledge group (18.9%). Not only were the perceptions regarding milk and dairy products more positive in the high-knowledge group (P<0.05), but the intake frequency of calcium-rich foods, such as tofu, soybean, and anchovies, was also higher in this group compared to the low-knowledge group (P<0.05). Overall, the preference for all calcium-rich foods was positively correlated to their intake frequency (P<0.05). Nutrition education experience and the recognition of the need for such education were positively correlated with the bone health-related nutrition knowledge score (P<0.05). In conclusion, bone health-related nutritional knowledge can affect calcium-related dietary behavior and increase the intake of calcium-rich food of 20~30-year-old women and this can contribute to the prevention of osteoporosis. To improve bone health-related nutritional knowledge among young women, it may be important to provide nutrition education.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• Journal of The Korean Association For Science Education
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• v.32 no.8
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• pp.1345-1355
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• 2012

In this study, 15 evaluation questions about light were developed and presented to 30 middle school students, and then the theory of knowledge space was used to analyze the status of the middle school students' knowledge about light. Not only was the state of the students' knowledge about light intended to be measured by schematizing the knowledge hierarchy obtained; the data obtained were also intended to be used as basic materials to improve the teaching methods used. To achieve the purpose of this study, the evaluation results, the individual knowledge state, and the hierarchy of questions were analyzed. As a result, different knowledge structures were found in the individuals and groups, and it was determined that individuals and groups should be diagnosed differently. In addition, the implication that there is a connection between each question and the individual knowledge state was drawn.

• Journal of The Korean Association For Science Education
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• v.27 no.8
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• pp.699-710
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• 2007

During the last two decades, many researchers have attempted to understand pedagogical content knowledge (PCK). Now it is time to think about how to apply the theoretical aspects of PCK to practice. In an attempt to address this issue, it is indispensable to review the existing literature on teachers' knowledge bases and PCK. Therefore, the purposes of this paper are to look at how the concept of PCK has been developed and extended over the past two decades as well as to provide a shared understanding of PCK for the practical use of this concept in teacher education programs. The paper begins with a discussion of various models of teachers' knowledge as conceptualized by several renowned researchers, moves on to a review of existing research focusing on the knowledge of science teachers, then examines the literature on PCK as a critical part of teachers' professional knowledge, and finally concludes with an integrated operational definition of PCK that can be employed into designing teacher education programs.