• 한국지능시스템학회논문지
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• 제23권5호
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• pp.400-405
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• 2013

The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.

• Asia pacific journal of information systems
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• 제11권4호
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• pp.113-133
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• 2001

We propose utilizing a fuzzy cognitive map(FCM) to integrate tacit knowledge and explicit knowledge both of which are crucial to the success of knowledge management. Recently, explicit knowledge is getting more available as CRM and data mining approaches become popular as the advent of using database and the Internet technology. However, for the knowledge management to be successful, tacit knowledge should be seamlessly integrated with explicit knowledge seamlessly. The problem hindering such effort is how to find a vehicle facilitating transformation of explicit knowledge into tacit knowledge, and vice versa. FCM has been important method for representing tacit knowledge as a form of explict knowledge. In this respect, we suggest the detailed process about how to integrate explicit knowledge and tacit knowledge by using FCM. We gathered extensive set of data from the credit card company, and applied our proposed method. Results showed that our approach is robust and promising for the field of integrating two different kinds of knowledge.

• Asia pacific journal of information systems
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• 제15권3호
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• pp.65-90
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• 2005

One of the major goals of using knowledge management systems(KMS) is the sharing of knowledge. The intent of an individual to share his/her knowledge plays a crucial role in sharing quality knowledge in corporations. However, there is little research that addresses this relationship between the intent to share and the use of KMS both from a holistic perspective and with empirical data analyses. To understand major factors that affect both knowledge sharing intent and the use of KMS, we conducted a field study from eight companies in four different industries which had been using KMS for at least a year. Using confirmatory factor analysis and structured equation modeling techniques, we have analyzed the relationships among top management support, trust among peers, trust in the organizational hierarchy, incentives and rewards, knowledge-sharing intent, KMS quality, knowledge quality, and the use of KMS. The research results show that top management support and trust between peers enhance the intent of sharing knowledge. We also found that top management support, knowledge-sharing intent, incentives and rewards, and the quality of knowledge have positive relationships with the use of KMS.

• 한국전자거래학회지
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• 제5권2호
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• pp.143-150
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• 2000

This paper presents a methodology how to utilize management of research scheduling plan, processing, and results using Web Knowledge Database System, which integrates research knowledge management model under the Research ＆ Development Environment. The content of this paper consists of description on utilization of the Web Knowledge Database System, sharing of the Research Knowledge through design data review, communications, and management of research knowledge flow during the Research ＆ Development Period.

• International Journal of Knowledge Content Development & Technology
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• 제10권3호
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• pp.33-50
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• 2020

Knowledge recipes are packages of knowledge which arise from the process of combining the knowledge assets in the organization in distinctive ways. This involves converting them into useful outputs which are the ideal core competitive advantage enablers for companies. The major objective of this study was to propose a knowledge recipe for financial-sector state corporations in Kenya. The study adopted a convergent parallel mixed methods research design. Quantitative and qualitative data were collected using questionnaires and key informant interviews. The target population of the study was 1574 respondents drawn from all financial state corporations. A multistage sampling technique was used for the study. The first phase involved purposive sampling of the organizations to be studied whereby the four state corporations namely: Capital Markets Authority, Competition Authority of Kenya, Kenya Investment Authority, and Kenya Revenue Authority were identified. The second phase entailed stratified sampling of the respondents in three strata namely senior management team, knowledge management team, and general staff. The authors used a census of all senior management team and knowledge management staff while a simple random sampling technique was used for the general staff. By use of the Krejcie and Morgan table, the actual sample size was 358 respondents from all the four organizations. Data were collected using questionnaires and interview schedules. The qualitative data were analyzed using content analysis while the quantitative data were analyzed by the use of Ms. Excel and VOSviewer and presented using pie charts, bar graphs, and tables. The response rate for this study was 257 (72%). The study revealed that while most employees in the financial sector organizations understand their knowledge needs, knowledge types, knowledge uses and knowledge gaps, they do not have a universal knowledge recipe to facilitate effective knowledge management in their organizations. Consequently, the authors propose a universal knowledge recipe for the state corporations in the financial sector in Kenya. The ingredients of the recipe are legal-knowledge (18%), financial knowledge (15%), administrative knowledge (11%), best practice (10%), lessons learnt (8%), human resource knowledge (8%), research and statistics knowledge (7%), product knowledge (6%), policy and procedure knowledge (5%), ICT knowledge (4%), investor knowledge (3%), markets knowledge (2%), general knowledge (2%) and regulatory framework knowledge (1%).

• International Journal of Quality Innovation
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• 제9권2호
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• pp.99-117
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• 2008

High-tech industry has been the principal economic source for Taiwan in recent years. The characteristics of high-tech industries in Taiwan are changeable product markets, short product life cycles and high company attrition rate. In the globalization trend, the high-tech industry has gradually increased corporate competitiveness and reached the goal of sustainable operations through knowledge management, knowledge sharing and new product research and development. Firms have aggressively strengthened and integrated their internal and external resources and enhanced knowledge sharing to increase industry operational performance. Effectively strengthening the knowledge management operation and performance evaluation of knowledge sharing in Taiwan's high-tech industry has become a critical issue. In the selection of knowledge sharing Key Performance Indicators (KPI), this research divided the knowledge sharing indicators into representative strategic indicators such as organizational knowledge learning, organizational knowledge resources, organizational information capacity and organizational knowledge performance through screening using Factor Analysis. The characteristics of the constructs were interdependent. This research calculated and adjusted the correlation among the key performance knowledge sharing indicators using ANP and determined the relative weight of knowledge sharing.

• 지능정보연구
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• 제5권1호
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• pp.13-34
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• 1999

Building a knowledge base effectively has been an important research area in the expert systems field. A variety of approaches have been studied including rules, semantic networks, and frames to represent the knowledge base for expert systems. As the size and complexity of the knowledge base get larger and more complicated, the integration of knowledge based with database technology cecomes more important to process the large amount of data. However, relational database management systems show many limitations in handing the complicated human knowledge due to its simple two dimensional table structure. In this paper, we propose Object-Oriented Knowledge Store (OOKS), a knowledge base model on the basis of a frame sturcture using an object-oriented database. In the proposed model, managing rules for inferencing and facts about objects in one uniform structure, knowledge and data can be tightly coupled and the performance of reasoning can be improved. For building a knowledge base, a knowledge script file representing rules and facts is used and the script file is transferred into a frame structure in database systems. Specifically, designing a frame structure in the database model as it is, it can facilitate management and utilization of knowledge in expert systems. To test the appropriateness of the proposed knowledge base model, a prototype system has been developed using a commercial ODBMS called ObjectStore and C++ programming language.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권1호
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• pp.1-7
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• 2011

Most of expert systems use the text-oriented knowledge bases. However, knowledge management using the knowledge bases is considered as a huge burden to the knowledge workers because it includes some troublesome works. It includes chasing and/or checking activities on Consistency, Redundancy, Circulation, and Refinement of the knowledge. In those cases, we consider that they could reduce the burdens by using relational database management systems-based knowledge management infrastructure and convert the knowledge into one of easy forms human can understand. Furthermore they could concentrate on the knowledge itself with the support of the systems. To meet the expectations, in this study, we have tried to develop a general-purposed knowledge conversion tool for expert systems. Especially, this study is focused on the knowledge conversions among text-oriented knowledge base, relational database knowledge base, and decision tree.

• 지식경영연구
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• 제1권1호
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• pp.81-90
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• 2000

This study proposes the knowledge discovery process for the effective mining of knowledge on the web. The proposed knowledge discovery process uses the Prior knowledge base and the Prior knowledge management system to reflect tacit knowledge in addition to explicit knowledge. The prior knowledge management system constructs the prior knowledge base using a fuzzy cognitive map, and defines information to be extracted from the web. In addition, it transforms the extracted information into the form being handled in mining process. Experiments using case-based reasoning and neural network" are performed to verify the usefulness of the proposed model. The experimental results are encouraging and prove the usefulness of the proposed model.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2003년도 종합학술대회 논문집
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• pp.197-203
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• 2003

The purpose of the study is to develop an integrated knowledge structuring system for the domain of engineering, in which ontology-based literature mining, knowledge acquisition, knowledge integration, and knowledge retrieval are combined using XML-based tag information and ontology management. The system supports combining different types of databases (papers and patents, technologies and innovations) and retrieving different types of knowledge simultaneously. The main objective of the system is to facilitate knowledge acquisition and knowledge retrieval from documents through an ontology-based dynamic similarity calculation and a visualization of automatically structured knowledge. Through experimentations we conducted using 100,000 words economic documents reported in the "Go！ Japan" project for analyzing Japanese industrial situation, and 100,000 words molecular biology Papers, we show the system is Practical enough for accelerating knowledge acquisition and knowledge discovery from the information sea.