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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2003.11
- 수록면
- 99 - 104 (6page)
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이 논문의 연구 히스토리 (3)
2003
RDB-based Automatic Knowledge Acquisition and Forward Inference Mechanism for Self-Evolving Expert Systems
Jin Sung Kim
한국지능시스템학회 논문지
2003.12
학술저널
Self-Evolving Expert Systems based on Fuzzy Neural Network and RDB Inference Engine
Jin Sung Kim
지능정보연구
2003.11
학술저널
Data Mining and FNN-Driven Knowledge Acquisition and Inference Mechanism for Developing A Self-Evolving Expert Systems
Jin Sung Kim
한국지능정보시스템학회 학술대회논문집
2003.11
학술대회자료
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초록· 키워드
오류제보하기
In this research, we proposed the mechanism to develop self 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, they 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.
목차
Abstract
1. Introduction
2. Methodology
3. Implementations
References
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UCI(KEPA) : I410-ECN-0101-2009-003-013676535