• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.568-571
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• 2004

In this study, we propose an efficient expert system (ES) construction mechanism by using dynamic knowledge map (DKM) and database management systems (DBMS). Generally, traditional ES and ES developing tools has some limitations such as, 1) a lot of time to extend the knowledge base (KB), 2) too difficult to change the inference path, 3) inflexible use of inference functions and operators. First, to overcome these limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. Then, elation database (RDB) and its management systems will help to transform the relationships from diagram to relational table. Therefore, our mechanism can help the ES or KBS (Knowledge-Based Systems) developers in several ways efficiently. In the experiment section, we used medical data to show the efficiency of our mechanism. Experimental results with various disease show that the mechanism is superior in terms of extension ability and flexible inference.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.567-574
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• 2011

The purpose of this paper is to identify factors that influence knowledge sharing and determine the attitudes of quantity surveyors towards knowledge sharing based on the factors. The analysis was based on an online questionnaire survey of Registered Quantity Surveyors from Selangor and Kuala Lumpur. Individualism and collectivism were identified as two major factors that influence attitude towards knowledge sharing. Indicators of individualism include individual attitude, competitiveness, care, incentives and rewards; while the indicators of collectivism are trust, social behaviors and motivation. The findings show that the level of attitudes towards knowledge sharing among quantity surveyors is generally high under enabling organizational environment. However, this is a cautious conclusion as the valid sample on which the analysis is based is relatively small. Willingness to share was found to be highest when incentives and rewards are involved as well as when there is a knowledge management system to promote continuous learning and sharing of knowledge.

• Proceedings of the CALSEC Conference
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• 1998.10a
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• pp.129-140
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• 1998

ㆍ ECRC ㆍ Knowledge Management ㆍ KM technologies ㆍ KBTS System -Mistakes KMS -Discussion KMS -Distinguished Features(omitted)

• Knowledge Management Research
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• v.7 no.2
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• pp.69-96
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• 2006

The purpose of this study is to find success factors of knowledge management in Korean firms, confirm them empirically, and verify their relative importance in terms of company size and industry type. The major studies on the knowledge management were deliberately selected and interpretively analyzed to find the success factors of Korean firms. As a result of the analysis, five success factors(top management's will, evaluation reward, organizational culture, knowledge management system, organizational structure) have been found. The empirical researches to make certain whether the above five factors derived are actually true or not have been separately performed by using questionnaire method. Based on the data collected, it is found that all five factors are significant. The degree of relative importance among the success factors of knowledge management in Korean firms has been found as: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v)organizational structure. In company size, large firm's degree of relative importance among the success factors are: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v) organizational structure. And medium-small firm's degree of relative importance among the success factors of knowledge management in Korean firms has been found as: (i)top management's will, (ii)organizational culture, (iii) evaluation-reward, (iv)knowledge management system, (v)organizational structure. Finally, in type of industry, manufactural firm's degree of relative importance among the success factors of knowledge management in Korean firms has been found as: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v)organizational structure. And non-manufactural firm's degree of relative importance among the success factors of knowledge management in Korean firms are: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v)organizational structure.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.229-251
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• 2005

Many Korean financial firms have built knowledge management systems, however, most of the systems have fragmentary ideas or suggestions. This study proposes the model of ontology-based knowledge repository through which one could integrate knowledge for business use in the BPM environments. The knowledge includes implicit and explicit knowledge. library materials, documents, and information for experts as well. In order to get basic ideas for this model, case studies utilized interviews and surveys were conducted targeting at four Korean banks' knowledge managers, librarians and thirty staffs.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.19-25
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• 2005

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.153-160
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• 1994

In this paper, we present the application of knowledge-based system technique for generating of PLC ladder diagram The developed prototype system receives a time chart as an input and generates a ladder logic as its output This results in the computerization and intellegent processing of PLC programming. The system can be effectively applied to sequence control where the PLC programs need to be frequently changed and generated.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.102-106
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• 1997

Grinding has difficulty in satisfying the qualitative knowledge based on the skilled expert as well as quantitative data for all user. Design of grinding database is based on the frame-based model for utilizing the empirical and qualitative knowledge. Inthis paper, basic strategy to develop the grinding database by frame-based model, which is strongly dependent upon experience and intuition, frame-base model, which is strongly dependent upon experience and intuition, is described. Design of grinding database is based on the frame-based model for utilizing the ambiguous knowledge and inference is accomplised by the object-oriented paradigm system.