• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.632-638
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• 2005

This paper describes a new data-handing, based on a Spatial Human Interface as human indicator, to the Spatial-Knowledge-Tags (SKT) in the spatial memory the Spatial Human Interface (SHI) is a new system that enables us to facilitate human activity in a working environment. The SHI stores human activity data as knowledge and activity history of human into the Spatial Memory in a working environment as three-dimensional space where one acts, and loads them with the Spatial-Knowledge-Tags(SKT) by supporting the enhancement of human activity. To realize this, the purpose of SHI is to construct new relationship among human and distributed networks computers and sensors that is based on intuitive and simultaneous interactions. In this paper, the specified functions of SKT and the realization method of SKT are explained. The utility of SKT is demonstrated in designing a robot motion control.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.9-12
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• 2004

Developing an efficient knowledge base construction mechanism as an input method for expert systems (ES) development is of extreme importance due to the fact that an input process takes a lot of time and cost in constructing an ES. Most ES require experts to explicit their tacit knowledge into a form of explicit knowledge base with a full sentence. In addition, the explicit knowledge bases were composed of strict grammar and keywords. To overcome these limitations, this paper proposes a knowledge conceptualization and construction mechanism for automated knowledge acquisition, allowing an efficient decision. To this purpose, we extended traditional knowledge map (KM) construction process to dynamic knowledge map (DKM) and combined this algorithm with relational database (RDB). In the experiment section, we used medical data to show the efficiency of our proposed mechanism. Each rule in the DKM was characterized by the name of disease, clinical attributes and their treatments. Experimental results with various disease show that the proposed system is superior in terms of understanding and convenience of use.

• The Journal of Information Systems
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• v.2
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• pp.127-140
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• 1993

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.737-744
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• 2004

Retrieving relevant information well corresponding to the user`s request from web is a crucial task of search engines. However, most of conventional search engines based on pattern matching schemes to queries have a limitation that is not easy to provide results corresponding to the user`s request due to the uncertainty of queries. To overcome the limitation in this paper, we propose a framework for knowledge-based semantic meta-search engines with the following five processes: (i) Query formation, (ii) Query expansion, (iii) Searching, (iv) Ranking recreation, and (v) Knowledge base. From simulation results on english-based web documents, we can see that the Proposed knowledge-based semantic meta-search engine provides more correct and better searching results than those obtained by using the Google.

• The Journal of Information Systems
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• v.18 no.3
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• pp.135-154
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• 2009

As the knowledge is recognized as a core factor of organization's competitiveness and creation of value added, the importance of knowledge management is also increased. To achieve the successful knowledge management, it is important to establish strategy that consider essential purpose of knowledge management such as creating and sharing of knowledge resource, improving performance, and continuing organizational innovation within the organization and influence factor inside and outside of organization. Until now, however, the research for knowledge management strategy was mostly limited to the statistical analysis based on the unilinear causality model, and systematic access and analysis that consider interaction and feedback structure between factors. In this paper, we developed the novel decision-making framework for successful strategy establishment by applying the analytic network process(ANP). Specifically. we derive clusters and components to decide the interaction and feedback structure between the elements of knowledge management by literature studies. And we produced relative importance and preference of clusters, components and alternatives dealing with feedback structure through the survey of experts in the field or related one of knowledge management. In result of this study, we expect that it will help the knowledge officer to decide establishing knowledge management strategy.

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

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.45-71
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• 1997

There has been considerable research recently on uncertainty handling in the fields of artificial intelligence and knowledge-based system. Various numerical and non-numerical methods have been proposed for representing and propagating uncertainty in knowledge-based system. The Bayesian method, the Dempster-Shafer's Evidence Theory, the Certainty Factor model and the Fuzzy Set Theory are most frequently appeared in the knowledge-based system. Each of these four methods views uncertainty from a different perspective and propagates it differently. There is no single method which can handle uncertainty properly in all kinds of knowledge-based systems' domain. Therefore a knowledge-based system will work more effectively when the uncertainty handling method in the system fits to the system's environment. This paper proposed a framework for selecting proper uncertainty handling methods in knowledge-based system with respect to characteristics of problem domain and cognitive styles of experts. A schema with strategic/operational and unstructured/structured classification is employed to differenciate domain. And a schema with systematic/intuitive and preceptive/receptive classification is employed to differenciate experts' cognitive style. The characteristics of uncertainty handling methods are compared with characteristics of problem domains and cognitive styles respectively. Then a proper uncertainty handling method is proposed for each category.

• Knowledge Management Research
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• v.12 no.1
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• pp.17-37
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• 2011

This paper is aimed at investigating the influence of the learning organization's structural characteristics, learner's characteristics, and perceived organizational support (POS) on organizational knowledge creation. also the POS is tested as a moderator on the relationship between learner's characteristics including learning goal orientation and learning self-efficacy and organizational knowledge creation. the results are as follows. for main effect hypotheses, both connecting the organization to its environment and establishing systems to capture and share learning system representing learning organization's structural characteristics have significant positive impact on organizational knowledge creation. the POS also has a significant impact on organizational knowledge creation. However, learning goal orientation and learning self-efficacy have not significant impact on organizational knowledge creation. for moderating effect hypothesis, POS moderates the relationship between learning goal orientation and organizational knowledge creation, which means if the POS is high then learning goal orientation has more significant positive impact on it. Based on our findings, we conclude that structural characteristics of learning organization provide organizations with an opportunity of knowledge creation. in particular, interconnectedness of organization with environment and organizational knowledge sharing systems determine the ways of behaving that are related to learning within organizations. however, learner's characteristics did not have a significant effect on organizational knowledge creation, which could be interpreted due to the fact that employees are not motivated to create new knowledge if they are rarely required to involve challenging works, generate new knowledge, or share preexisted knowledge with others.

• The Journal of Information Systems
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• v.28 no.4
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• pp.377-401
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• 2019

Purpose The purpose of this study is to investigate the effects of learning expectation and perceived knowledge sharing on user satisfaction and IS continuance in the Korean Army which is currently using the Regiments' Information System to help their Integrated Administration Management. Based on both the Information System(IS) Continuance Model and IS Success Model, this study also examine the role of system quality on user satisfaction. We develop a research model(structural equation model) and its hypotheses that learning expectation, perceived knowledge sharing, and system quality increase users' satisfaction, which leads to IS continuance. The effect of learning expectation on perceived knowledge sharing is also hypothesized. Design/methodology/approach Online Survey using e-mails was administered to test our research model and associated hypotheses. Among the 360 e-mail letters including our survey questionnaire, 285 responses were collected via e-mails. Meaningful 225 cases were analyzed for our study. SPSS Statistics 24.0 and SmartPLS 3.0 were used to analyze both measuremant test and hyotheses test by using the data set. Findings Survey results show that learning expectation(confirmation variable), learning expectation, perceived knowledge sharing(a perceived usefulness variable), and system quality(a system characteristic) each increases user satisfaction, which leads to IS continuance, under the control of the effect of habit to use information systems. Learning expectation also has a positive influence on perceived knowledge sharing. Theoretical and practical implications are presented.

• The Journal of Information Systems
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• v.19 no.4
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• pp.167-181
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• 2010

Although This study was designed to evaluate internet communities based on knowledge creation and learning. To do so, we created and tested our research model, by using selected 20 different knowledge communities. Using the K-Means Clustering techniques, we found different characteristics and evaluated these characteristics by the criterion. The results of discriminant analysis suggested 4 different models such as 'Search Engine,' 'Open Communities,' 'Specialty Communities,' and 'Activity Communities.' The results of this study indicated that it can cover some reasons for development process of knowledge communities and that it can also create a strategic framework for practical use of knowledge communities.