• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.583-588
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• 1989

In this paper, a modelling methodology for a robotic workcell is proposed and compared with the conventional Petri nets model. Also, a method for managing the cell operation is described through the knowledge base. The knowledge bases for state transition and assembly job information are obtained from the state transition map(STM) and the assembly job tree(AJT), respectively. Using the knowledge base, the system structure is discussed in both managing the cell operation and evaluating the various performance. Finally, a simulation algorithm is presented with the simulation results to show the effectiveness of the proposed modelling approach.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.245-251
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• 1999

In this study, by using the natural language processing of the field artificial intelligence, automated index was performed . And then, the Natural Language Processor for Constructing Database (NALPDB) has been developed. Furthermore, the Design knowldege Information Relational DataBase (DIREDB) has been also developed, which is designed to interlock the knowledge base. DIREDB processes both the documented design-data , like a concrete standard specification, and the design knowledge frrom an expert. DIREDB is also simulates the design space of structures accordance with the production rule, and thus it is determined that DIREDB can be used as a engine to retrieve new knowledge and to implement knowldege base that is necessary to the development of automatic design system.

• 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 Institute of Industrial Engineers
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• v.14 no.2
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• pp.53-61
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• 1988

Games such as baseball, soccer are scheduled by a given game type such as tournament, league or their mixed form. The objective of this paper is to find an efficient game-scheduling method with respect to traveling distance, break-time and other conditions. In this paper we first present two models which minimize traveling distance. The first model that a match is played once each other is solved by a heuristic method. In the second model that a match is played more than once, teams are paired by a modified 0 - 1 programming, and the pairs are rearranged in order to generate a number of workable schedules. Then Expert Systems is applied to solve breake-time and other conditions. In order to represent expertise's knowledge effectively, we present a new design of knowledge-base and data-base, inference engine including many rules and meta-rules which controls the global system. In knowledge-base, binary relation among various attributes is used to ease not only knowledge acquisition but also system execution.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.30 no.6
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• pp.40-46
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• 2012

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.208-212
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• 2001

This paper is for the optimized management of the knowledge abstracted from the World-Wide Web(WWW) in which we assume the infinite knowledge-base. Though we can abstract various useful knowledge such as the facts and the rules from the WWW pages, they may include many noisy knowledge. Therefore we have to reasonably reject them from the knowledge-base which is composed of knowledge abstracted from the WWW. To do this, we propose the oblivious memorization concept. This concept is characterized by the memorization based on the oblivion mechanism of human being. We assume the memorization is the function of the concern for any knowledge, oblivion ability and time. That is, the more concern for my knowledge the ore memorizable. And, the more oblivious and the more tine spent the less memorizable by exponentially. Where, tie assume the oblivion is the function of the degree of previous memorization, memorization ability md the number of knowledge stimulation. That is, the more previously memorized, the greater memorizing ability and the more frequently stimulated by any knowledge the less knowledge oblivious.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.125-132
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• 2006

The construction of an emergency generator's diagnosis system for the preparation of emergency in nuclear plant is vital. To construct a knowledge base of the diagnosis system, the classes and a causality model should be designed. In order to design those elements, at first. object of the diagnosis system should be defined. After the investigation of normal and abnormal states. the external knowledge such as entities and activities is extracted, that the operational principle of the system. For the conversion of the extracted external knowledge to the internal one, the entities are defined as classes and the activities converted into the causality. Through the recursive configuration of the causality and proper examination, the diagnosis knowledge applicable to the knowledge base is completed. In this paper, it is possible to construct a knowledge base with high portability since the independence of design model is considered through the decision table.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.377-380
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• 2011

음악은 인류의 대표적인 예술로서 오랜 세월동안 사랑을 받아왔다. 그 오래된 세월만큼이나 인류가 만들어온 음악의 수는 방대하다. 방대한 음악이 IT기술의 발달과 인터넷의 확산을 통하여 온라인 음악시장을 형성하였고 음악은 디지털 음원으로 관리되게 되었다. 이러한 디지털 음원을 효과적으로 검색하기 위한 방법은 많이 연구되었다. 그리고 검색을 도와줄 대량의 디지털 음원 자료들을 저장하고 관리하는 기법에 관한 연구가 필요하다. 본 논문에서는 대용량 자료를 처리하는 기술로 관심 받고 있는 하둡을 통하여 이 문제를 연구하였다. 하둡의 맵리듀스, HDFS 그리고 HBase를 이용하여 음악 내용기반검색을 설계하였다. 본 시스템은 음악 검색 시스템을 관리하고 유지하는데 있어서 컴퓨팅자원을 절약함으로써 비용을 절감 효과를 얻을 수 있다.