• Journal of Digital Convergence
• /
• v.3 no.1
• /
• pp.183-194
• /
• 2005

This study is aimed at building up an Ontology to solve the discrepancy of terminologies between teachers and students by showing, through Ontology, the knowledge for diagnosis of underachieving students. Also this study makes it possible to infer the diagnosis based on information of these underachieving students. In addition, while a general Underachieving Students diagnosis system shows special diagnosis, this Ontology system helps users obtain correct concepts through this knowledge based system, and suggest building an Ontology to extend unclear conceptual knowledge to clearer ones.

• Journal of Intelligence and Information Systems
• /
• v.14 no.2
• /
• pp.179-192
• /
• 2008

This case study shows constructing the knowledge-based system using a rule-based approach for detecting illegal transactions regarding money laundering in the Korea Financial Intelligence Unit (KoFIU). To better manage the explosive increment of low risk suspicious transactions reporting from financial institutions, the adoption of a knowledge-based system in the KoFIU is essential. Also since different types of information from various organizations are converged into the KoFIU, constructing a knowledge-based system for practical use and data management regarding money laundering is definitely required. The success of the financial information system largely depends on how well we can build the knowledge-base for the context. Therefore we designed and constructed the knowledge-based system for anti-money laundering by committing domain experts of each specific financial industry co-worked with a knowledge engineer. The outcome of the knowledge base implementation, measured by the empirical ratio of Suspicious Transaction Reports (STRs) reported to law enforcements, shows that the knowledge-based system is filtering STRs in the primary analysis step efficiently, and so has made great contribution to improve efficiency and effectiveness of the analysis process. It can be said that establishing the foundation of the knowledge base under the entire framework of the knowledge-based system for consideration of knowledge creation and management is indeed valuable.

• Hwankyungkyoyuk
• /
• v.20 no.1
• /
• pp.118-130
• /
• 2007

This article investigated the effects of students' knowledge, beliefs, and attitudes about environment on environmental issue-based environmental problem solving in secondary school students. This article also investigated whether these factors are related to the environmental problem solving. Therefore, we first developed the test instruments to measure secondary school students' environmental knowledge, environmental beliefs, and environmental attitudes. Then, we developed the environmental issue-based teaching materials for secondary school students. The subjects participated in a environmental issue-based approach that consist of 3 lesson over an three weeks period. The results of this study presents that environmental knowledge and environmental beliefs are effective for the environmental problem solving in middle school environment class. First, secondary school students knew well ecological knowledge about environmental problem, and was entertaining most ecological beliefs. Second, secondary school students were having attitude which is most pro-environmental behaviors. Third, secondary school students are referring necessity of development partially about environment point at issue of country level. But, they were retaining situation of preservation about environment point at issue of area level. It's not easy to generalize these results because of many different variables, but the results suggests that teachers should make an effort to improve student's environmental problem solving ability applying environmental issue-based learning. And it's necessary to develop many kinds of environmental issues and teacher's instruction method to enrich problem solving ability in the process of environmental issue-based learning.

• Information Systems Review
• /
• v.5 no.2
• /
• pp.151-168
• /
• 2003

A knowledge portal is an integrated gateway for accessing relevant knowledge, collaborating and communicating with other users, and also linking internal applications which is becoming crucial in the age of information abundance. Research and development is a typical knowledge-intensive activity. However, knowledge management support in R&D has been minimal in most research organizations. In this paper, a knowledge portal is designed to support team-based researches in science and technology for searching and browsing knowledge, and also communicating with other team members, coordinating research project and collaborating with other researchers. Automating knowledge acquisition from various knowledge sources, knowledge categorization by applying text categorization method, and knowledge recommendation can help to relieve management effort and increase the efficiency of knowledge management processes. A prototype system based on the suggested model is also presented.

• Knowledge Management Research
• /
• v.7 no.2
• /
• pp.13-34
• /
• 2006

This paper is an exploratory study to analyze the flow of knowledge in science and technology in order to predict technology innovation. Here, we need to look into the characteristics of how knowledge is created in science, technology and industry to start with. Based on the characteristics we find, we have to understand the relationships between science, technology and industry, and construct a model to link them to each other for future empirical studies. In this study, we take a general view of the existing study results and theoretical models on the characteristics and linkage of scientific and technological knowledge. Moreover, we examine the preliminary framework to link science to technology to industry for further study on the knowledge flow of science and technology. Finally, we present the direction for future study by using the examined models and framework.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.48
• /
• pp.213-222
• /
• 1998

The objective of this study is to develop a knowledge-based scheduler applying simulation and knowledge base. This study utilizes a machine induction to build knowledge base which enables knowledge acquisition without domain expert. In this study, the best job dispatching rule for each order is selected according to the specifications of the order information. And these results are built to the fact base and knowledge base using the attribute-oriented induction method and simulation. When a new order enters in the developed system, the scheduler retrieves the knowledge base in order to find a matching record. If there is a matching record, the scheduling will be carried out by using the job dispatching rule saved in the knowledge base. Otherwise the best rule will be added to the knowledge base as a new record after scheduling to all the rules. When all these above steps finished the system will furnish a learning function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.461-467
• /
• 1998

Although many methods of knowledge acquisition has been developed in the expert systems field, such a need for causal knowledge acquisition has not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approcach, we prototyped a causal knowledge-driven inference engine named CAKES and then experime ted with some illustrative examples.

• Industrial Engineering and Management Systems
• /
• v.14 no.1
• /
• pp.73-87
• /
• 2015

The development of a practical technology intelligence system requires a knowledge base that structures the core information and its relationship distilled from large volumes of technical data. Previous studies have mainly focused on the methodological approaches for technology opportunities, while little attention has been paid to constructing a practical knowledge base. Therefore, this study proposes a procedure to construct a function-based knowledge base for technology intelligence. We define the product-function-technology relationship and subsequently present the detailed steps for the knowledge base construction. The knowledge base, which is constructed analyzing 1110582 patents between 2009 and 2013 from the United States Patent and Trademark Office database, contains the functional knowledge of products and technologies and the relationship between products and technologies. This study is the first attempt to develop a large-scale knowledge base using the concept of function and has the ability to serve as a basis not only for furthering technology opportunity analysis methods but also for developing practical technology intelligence systems.

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

• Proceedings of the Korea Database Society Conference
• /
• 2008.05a
• /
• pp.13-30
• /
• 2008

The objective of this study is to explore a knowledge sharing typology for Internet portals based on knowledge-based view of firm. Furthermore, we provide insights into how the evolution of Internet portals takes place by describing user behavior of knowledge sharing. For doing this, we first present a typology of knowledge sharing based on the two dimensions such as knowledge donation and knowledge collection. Then we conduct case study of the Korean major portals to demonstrate a proposed typology. The main finding of the analysis is that three distinctive types of knowledge sharing patterns within portals are distinguished: collaboration, accumulation, and publishing. We conclude that user behavior of knowledge sharing is characterized as guiding factors in evolution process.