• 한국전자거래학회지
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• 제6권1호
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• pp.101-121
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• 2001

MOCT(Ministry of Construction St Transportation) suggested "A Plan for Implementation of Knowledge-based Economy for the Construction Industry" to add high value to a construction industry. by means of knowledge-based management. However, there is no knowledge management system which considers characteristics of a domestic construction industry. So a useful knowledge vanished the moment a company terminated a construction Uoject. Therefore, it is necessary to develop "An application method of a knowledge management system for the construction industry" which takes a real environment into consideration. This study aims to present the road map of a knowledge management system which helps the bodies of the construction industry(government, research center/university, enterprise, etc) to do a knowledge activity efficiently. To accomplish this objective, we analyzed overall knowledge management activities. In addition we suggested an implementation direction and strategy of a knowledge management system for the construction industry. As results of this study, we presented the road map of a knowledge management system for the construction industry, which is composed of policy forum, study forum, professional forum, research forum and knowledge 8t information DB. We expect that results of this study will be used as a basis for implementing the knowledge portal that brings a construction industry to a high value added industry.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• 지식경영연구
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• 제14권3호
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• pp.87-100
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• 2013

As the external knowledge networks of firms have become more and more important to their product and service innovations, many global leading companies such as P & G, IBM, and Samsung Electronics have formulated and implemented their open innovation strategy. This study attempts to empirically analyze the effects of CEOs' supports for external knowledge networks, external knowledge network-oriented cultures and inter-organizational knowledge management systems as the major antecedents to external knowledge networks by using the data based on organizational members' perceptions about them. Based on 847 samples collected from employees in three companies in the medical, the construction and the IT service industries, this study performed a structural equation modeling (SEM) analysis about the effects of the antecedents to the external knowledge networks on product and service innovations through Partial Least Squares (PLS). The empirical findings of this study show that CEOs' supports for external knowledge network positively influence product and service innovations, partially mediated by external knowledge network-oriented cultures and inter-organizational knowledge management systems. And they also show that external knowledge network-oriented cultures and inter-organizational knowledge management systems have a positive effect on product and service innovations, respectively, partially mediated by external knowledge networks. With these new findings, academic and practical implications are discussed.

• 디지털산업정보학회논문지
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• 제6권1호
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• pp.91-103
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• 2010

This paper introduces efficient rule-based semantic search techniques to ontology-based knowledge commerce services. Primarily, the search techniques presented in this paper define rules of reasoning that are required for users to search using the concept of ontology, multiple characteristics, relations among concepts and data type. In addition, based on the defined rules, the rule-based reasoning techniques search ontology for knowledge commerce services. This paper explains the conversion rules of query which convert user's query language into semantic search words, and transitivity rules which enable users to search related tags, knowledge products and users. Rule-based sematic search techniques are also presented; these techniques comprise knowledge search modules that search ontology using validity examination of queries, query conversion modules for standardization and expansion of search words and rule-based reasoning. The techniques described in this paper can be applied to sematic knowledge search systems using tags, since transitivity reasoning, which uses tags, knowledge products, and relations among people, is possible. In addition, as related users can be searched using related tags, the techniques can also be employed to establish collaboration models or semantic communities.

• 한국산학기술학회논문지
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• 제2권2호
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• pp.33-37
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• 2001

본 논문은 사출성형제품의 게이트 설계를 합리적으로 수행하기 위해 사출금형설계전문가의 축적된 지식과 경험을 발췌·정리하여 지식베이스를 작성하였으며, 설계에 필요한 제품의 형상정보를 제공하기 위해 총칭형상과 특징형상 개념을 이용하여 특징형상을 정의하였다. 또한 지식베이스를 통해 산출된 설계결과를 기하학적 모델러와의 인터페이스를 통해 3차원 형상으로 보여지도록 하였으며 최종 생성된 3차원 기하학적 형상정보는 CAE 모듈이나 CAPP 모듈에서의 후속작업을 위해 제공될 수 있도록 구축되었다.

• 한국CDE학회논문집
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• 제10권1호
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• pp.49-60
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• 2005

Existing CAD systems have configured geometry data and it is necessary to extend the configured geometry into a knowledge-based system. An intelligent CAD system emerged to provide such a knowledge-based system. However the intelligent CAD system has a limited product model to represent various knowledge models. This paper presents a model, called extended intelligent CAD model, which can extend the product model of the intelligent CAD system into further detailed knowledge model. The extended intelligent CAD model includes a whole design process knowledge and an efficiency of the model has been verified via a knowledge based wiper design system. The model can improve the functionality and efficiency of the existing CAD systems.

• 산업공학
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• 제13권1호
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• pp.54-59
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• 2000

Presented in this paper is a development of knowledge management system based on knowledge life cycle. Knowledge processes in an organization have a life cycle from creation to disposal. So, KMSs have to support the entire life cycle of knowledge. This paper proposes desired knowledge life cycle model, and extracted functional requirements for KMS. For the fulfillment of this requirements, we developed KMS called XM-Brenic/MSX. This system has 6 components for supporting the knowledge life cycle.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.781-782
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• 2007

• 지능정보연구
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• 제9권2호
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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.

• 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.