• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.88-98
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• 2000

Cooperative query answering provides neighborhood or associate information relevant to the initial query using the knowledge about the query and data. In this paper, we present an intelligent query answering system for suporting cooperative query answering system presented in this paper performs query relaxation process using hybrid knowledge base. The hybrid knowledge base which is used for relaxation of queries, composes of semantic list and rule based knowledge base for structural approach. Futhermore, this paper proposes the query relaxation algorithm for query reformulation using initial query on the basis of hybrid knowledge base.

• Fashion & Textile Research Journal
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• v.11 no.4
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• pp.648-660
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• 2009

In this research, an expert system has been developed to select optimum well-matched fusible interlinings with a face fabric. First, a database of face fabrics and fusible interlinings has been constructed. And knowledge acquisition has been performed from the previous studies about the properties of fusible interlinings and fused composites as well as fusing prsocess quality control. Then, a rule-based knowledge-base has been constructed through knowledge classification. Finally, we have constructed an inference engine with the knowledge-base. The expert system enables us to easily select optimum fusible interlinings for a face fabric considering high quality fused composites and fashion trend.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2075-2082
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• 1998

Rule-based knowledge representHtion is, the most popular technique for ,storage and manipulation of domain knowledge in expert system. By the way, the amount of knowledge increases more and more in this representatiun technique, it, relationship becomes complex, and even its contents can be modified. This is the reason why rule-based knowledge representation technique requires a verification ,system which can maintain consistency and completeness of knowledge base. This paper is to propose a verification system for consistency and completeness of knowledge base to promote the efficiency and reliability of expert system. After verifying the potential errors both in structure and in semantics whenever a new rule is added, this system renders knowledge base consistent and complete by correcting them automatically or by making expert correct them if it fails.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.6
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• pp.187-196
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• 2001

In this study, by using the natural language processing of the field of artificial intelligence, automated index was performed. And then, the Natural Language Processing Interface for knowledge representation(NALPI) has been developed. Furthermore, the DEsign KnOwledge DataBase(DEKODB) has been also developed, which is designed to interlock the knowledge base. The DEKODB processes both the documented design-data, like a concrete standard specification, and the design knowledge from an expert. The DEKODB is also simulates the design space of structures accordance with the production rule, and thus it is determined that DEKODB can be used as a engine to retrieve new knowledge and to implement knowledge base that is necessary to the development of automatic design system. The application field of the system, which has been developed in this study, can be expanded by supplement of the design knowledge at DEKODB and developing dictionaries for foreign languages. Furthermore, the perfect automation at the data accumulation and development of the automatic rule generator should benefit the unified design automation.

• The Journal of Information Systems
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• v.1
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• pp.149-178
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• 1992

The field of Artificial Intelligence (AI) is growing, and many firms are investing in expert system, one of AI's subfields. An expert system is defined as a computer program designed to replicate some aspect of the decision making of one or more experts and to be used by nonexperts. The kernel of an expert system is the knowledge base, which consists of the facts and rules that represent the expert's knowledge. Firms need expert systems for training employees to provide competitive advantage. This paper describes the model of an instructional expert training system which interfaces to external programs, such as an ASCII file, a work-sheet program, and a database program. A model for such an expert training system, and its prototype have been developed to demonstrate its functionality. A modular knowledge base has been developed and implemented in support of this study. The modularized knowledge base offers the user an easy and quick maintenance of facts and rules, which are frequently required to change in future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.185-195
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• 1997

In this paper, research and applications of a knowledge-base system in scheduling are review. The method is based on the problem-solving techniques developed in artificial intelligent. The object of this paper is to enable the re-time rescheduling under dynamic environments. Developed to KBS(Knowledge-Based Scheduling) system in this paper, will based on expert system, and applicate to requirement of users effectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.57-69
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• 2001

Expert system is one of AI area. It simulates the human's way of thinking to give solutions of problem in many applications. Most expert system consists of many components such as inference engine, knowledge base, and so on. Especially the performance of expert system depend on the control of efficiency of inference engine. Inference engine has to get features; first, if possible to minimize restrictions when it constructed the knowledge base. second, it has to serve various kinds of inferencing methods. In this paper we propose knowledge scheme for representing domain knowledge in ease, knowledge implementation technique for inferencing, and integrated knowledge-base engine with blackboard and inference engine. And we describe a expert system prototype that implemented in this paper using proposed methods, it perform diagnose about heavy industrial device. The fault diagnosis system prototype has been studied in this paper will be practical foundation in the research area of knowledge based system.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1326-1334
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• 2019

Knowledge base means a library stored in computer system providing useful information or appropriate solutions to specific area. Knowledge base associated with autism is the complex multidimensional information set related to the disease autism for its pathogenic factor and therapy. This paper focuses on the knowledge of biological molecular information extracted from massive biomedical texts with the aid of widespread used machine learning methods. Six classes of biological molecular information (such as protein, DNA, RNA, cell line, cell component, and cell type) are concerned and the probability statistics method, conditional random fields (CRFs), is utilized to discover these knowledges in this work. The knowledge base can help biologists to etiological analysis and pharmacists to drug development, which can at least answer four questions in question-answering (QA) system, i.e., which proteins are most related to the disease autism, which DNAs play important role to the development of autism, which cell types have the correlation to autism and which cell components participate the process to autism. The work can be visited by the address http://134.175.110.97/bioinfo/index.jsp.

• 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.