• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.84-87
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• 2001

Severe accident management can be defined as the use of existing and alternative resources, systems, and actions to prevent or mitigate a core-melt accident in nuclear power plants. TRAIN (Training pRogram for AMP In NPP), developed for training control room staff and the technical group, is introduced in this paper. The TRAIN composes of phenomenological knowledge base (KB), accident sequence KB and accident management procedures with AM strategy control diagrams and information needs. This TRAIN might contribute to training them by obtaining phenomenological knowledge of severe accidents, understanding plant vulnerabilities, and solving problems under high stress.

• The Journal of Information Technology and Database
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• v.1 no.2
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• pp.3-24
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• 1994

기존의 데이타 모델 및 설계 방법론들은 실세계의 데이타 객체에 대해 고정된 한 측면의 모델 표현만을 허용하기 때문에 여러 측면으로 관측이 가능한 실세계 객체들의 표현에 어려움을 갖는다. 제시한 객체 중심 측면 모델(OOAM : Object-Oriented Entity Aspect Model)은 실세계의 각 객체에 대해 다수의 측면 표현을 가능하게 한 객체 중심의 데이타 모델로 데이타와 지식 표현에 유용한 모델이다. 데이타베이스 시스템과 지식베이스 시스템 중 어느 하나의 시스템이 다른 시스템의 특징을 빌리거나 통합할 수 있다면 두 시스템에게 서로 이득이 될 수 있다. 이러한 KB/DB(Knowledge Base/Data Base)의 통합은 최근에 객체 지향 개념과 연역 개념에 의해 연구가 활발히 진행되고 있다. 본 논문에서는 객체의 측면 개념을 제공하는 OOAM의 기본 개념을 보여주고 OOAM에 의해 구축되는 데이타베이스 스키마의 시맨틱을 분석하고 서술하기 위해 OOAM을 형식적으로 정의하였다. 그리고 KB/DB 통합에 관련된 연구들을 분석하고 데이타베이스에 관련된 지식의 종류를 서술한 후 OOAM을 사용하여 KB/DB 통합을 위한 지식베이스와 데이타베이스의 개발 방법론을 제시하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.336-341
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• 2002

현재 연구개발 중인 웹 정보 에이전트는 Agent Manager와 KB Manager. Web Manager로 구성되어 있다. 이 시스템은 동물영역에 관련된 정보를 영어로 서비스하고 있어 국내 접근보다는 외국에서의 접근이 더 많았다. 그러므로 국내 사용을 높이기 위해 애완용 동물을 위주로 한 정보추출(IE)을 수행하여 지식베이스(KB)의 확장을 시도하고 있다. 이를 위하여 태그(tag) 및 심볼(symbol)의 패턴(pattern) 유사성 정보를 찾아내고, 기존 KB와 연계하여 KB의 확장 및 수정에 이용하기 위한 유효 정보 패턴 결정에 활용함으로써 정보 추출의 새로운 방법을 고찰하고 그 가능성을 제시하고자 한다.

• 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.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed 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 former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have 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, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. 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, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.193-202
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• 2017

Development of Intelligent Systems involves effective integration of large-scaled knowledge processing and understanding, human-machine interaction, and intelligent services. Especially, in our project for development of a self-growing knowledge-based system with inference methodologies utilizing the big data technology, we are building a platform called WiseKB as the central knowledge base for storing massive amount of knowledge and enabling question-answering by inferences. WiseKB thus requires an effective methodology to analyze diverse requirements convoluted with the integration of various components of knowledge representation, resource management, knowledge storing, complex hybrid inference, and knowledge learning, In this paper, we propose an integrated requirement analysis method that blends the traditional sequential method and the iterative-incremental method to achieve an efficient requirement analysis for large-scale systems.

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.18-25
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• 1992

In this paper, a rule checker which aids in composing a consistent knowledge base by checking redundancies and inconsistencies in a knowledge base is proposed. The proposed algorithm checks the rules by representing the rule connections as a network . The standard model of the rules adapted in this algorithm is in the Conjunctive Normal Form which includes NOT's, and rules of conventional expert system can be checked by converting them into the standard form by a rule form at converter. When compared with Ginsberg's KB-reducer which is conceptually most similar to the proposed algorithm among existing methods,it is shown by a computer simulation that with 360 rules, the checking time is three times faster and the rate increased as the number of rules increased, but the total memory requirement of the proposed agorithm is 1.2 times larger. The proposed algorithm has further advantages in that it can check circular rule chains and can find the paths of the redundant and inconsistent rules.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.450-451
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• 2012

The need of Clinical Decision Support System (CDSS) in healthcare setup is increasing day by day. EHR Meaningful Use advocates CDSS as an important component of EHR/EMR systems. CDSS can be ranged from a simple to a very sophisticated system. The more complex CDSS systems need more attention to develop because of many reasons including its Knowledge Base (KB) structure/maintenance/evolution, inference capabilities and usability. Above all the KB maintenance and evolution is very crucial and important from the perspective of useful decision capabilities. Also the richness of the KB is important to cover the decision gaps handling a particular situation in the course of patient care. It cannot be expected from the clinicians to remember everything in regard to patient diagnosis and treatment. Similarly, it is also crucial for clinicians to keep themselves updated with the new research in the area. That is the reason they frequently require accessing to the online knowledge resources. Literature proved that online knowledge resources are capable providing answers to questions that might not be answered rely only on clinician wisdom and experience. This paper provides the theme of meaningful utilization of online knowledge resources in the context of diagnosis and treatment process for cancer patients more specifically Head and Neck cancer.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.101-107
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• 2006

In this research, we propose a hybrid inference architecture for medical diagnosis based on dynamic knowledge map (DKM) and relational database (RDB). Conventional expert systems (ES) and developing tools of ES has some limitations such as, 1) time consumption to extend the knowledge base (KB), 2) difficulty to change the inference path, 3) inflexible use of inference functions and operators. To overcome these Limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. The DKM also can help the knowledge engineers to change the inference path rapidly and easily. Then, RDB and its management systems help us to transform the relationships from diagram to relational table.