• Journal of Information Management
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• v.31 no.2
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• pp.1-10
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• 2000

Many experts predict that 21st century will be knowledge-based society. That is, knowledge beside labor, capital will be recognised as important assets and powerful competitiveness. The main aim of government-supported research institute is to evolve R&D activities and to diffuse them. And all process including R&D activities can be called to knowledge life cycle. This paper reviews understanding of KM and information professional's role in research information center. Sometimes, CEOs misunderstand that only building of knowledge management system is sucess of knowledge management initiatives but the most important factors of its success are human and culture of knowledge sharing, not H/W systems. Information professionals must be consultant, analyst, content manager, planner and marketer, knowledge manager to practice knowledge management initiatives successfully.

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.129-139
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• 2004

This society is rapidly changing into an information-oriented society. As such, revolutionary and efficient teaching methods are needed in school education rather than traditional methods. To be an efficient teaching lesson, teaching plans based on learners' prior knowledge are needed. The knowledge-space theory provides the methods of efficient analysis about learners' status of knowledge. This study designs and implements the support-learning tool based on the knowledge-space theory to increase the efficiency in classroom lessons through the development of various methods of analysis of learners' knowledge status.

• 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.

• Journal of Welding and Joining
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• v.17 no.3
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• pp.44-49
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• 1999

Power and industrial plant use various welding processes and many kinds of materials. Thus, it is a difficult task to get the proper welding information. In this research, a welding information system was developed to solve the difficulty. It consists of database system, knowledge base system and diagram analysis programs. Database system contains a large database and various searching method corresponding to the kind of information. A large part of welding information is managed by this database system. Knowledge based system is used for decision of proper welding process and analysis of weld defects. It has conversion program from text to knowledge, and inference mechanism. Finally, Diagram analysis programs carry out the calculation of ferrite content in the weld metal. By the calculation, a crack occurrence can be avoided. The developed system can be a useful tool for welding in the field of power and industrial plant.

• Annual Conference of KIPS
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• 2002.11a
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• pp.597-600
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• 2002

본 논문에서는 정보추출 및 정보검색, 문서요약과 같은 자연어처리 응용에서 중요한 역할을 하는 개체명 인식 모델을 제안하였다. 기존의 한국어 개체명 인식에 관한 연구는 규칙 기반 연구의 경우 수동으로 생성한 규칙이나 어휘사전에 매우 의존적이고, 통계기반의 연구의 경우 개체명이 태깅된 대량의 학습데이터를 필요로 하므로 새로운 도메인으로의 이식성 관점에서 한계가 있다. 이를 극복하기 위해 본 논문에서는 개체명이 태깅되지 않은 학습데이터를 이용하여 Cotraining 기반 학습을 수행함으로써 개체명 인식을 위한 규칙과 사전을 자동적으로 확장하였다. 실험 결과, 경제분야 문서에 대해 87.6%의 정확률을 보였다.

• Annual Conference of KIPS
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• 2013.05a
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• pp.117-120
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• 2013

스마트폰의 보급으로 모바일 소셜 네트워크 게임(SNG: Social Network Game)을 즐기는 사용자들이 증가하고 있다. 그러나, SNG의 특성에 맞는 자원 활용률을 고려한 효율적인 게임서버에 대한 연구는 매우 부족한 실정이다. 본 논문은 가상화 기반으로 SNG게임서버를 설계 및 구현한다. 또한 가상화 기반 분산 게임서버의 CPU 사용률과 Memory 사용량을 분석하여 게임서버의 자원 활용률을 보인다. 이를 토대로 SNG게임서버 환경구축의 기반지식을 제공한다.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.145-170
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• 2011

The purpose of this paper is to suggest a pilot model of knowledge organizing system which reflects the knowledge structure of research papers, using a case analysis on the "Korean Research Memory" of the National Research Foundation of Korea. In this paper, knowledge structure of the research papers in humanities and social science is described and the function of the "Korean Research Memory" for scholarly sense-making is analysed. In order to suggest the pilot model of the knowledge organizing system, the study also analysed the relation between indexed keyword and knowledge structure of research papers in the Korean Research Memory. As a result, this paper suggests 24 axioms and 11 inference rules for an ontology based on semantic relation of the knowledge structure.

• Journal of KIISE:Information Networking
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• v.37 no.2
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• pp.99-108
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• 2010

The Wireless Ad Hoc network is discussed as a core technology for ubiquitous computing, and the smart tag technology is currently being actively discussed as a part of the sensor network. Thus, considering its security may advance the realization of ubiquitous computing. RFID (Radio Frequency Identification) technology using the smart tag technology as a part of the sensor network is currently in the limelight. In particular, when RFID is applied to a knowledge management system managing various data, data mobility and management convenience are ensured and automated knowledge service can be provided to users. Accordingly, this paper to proposed a secure scheme for mobility knowledge management systems using multi-agents differentiated from the existing knowledge management systems. Specifically, the proposed scheme designates user's authentication and privilege information in multi-agents and provides effective knowledge service through grouping based on user information. Moreover, even user's movement, the proposed scheme ensures service availability and provides continuous information through communication with multi-agent systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.35-41
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• 2017

In this paper, we analyze the performance of the recently introduced Hint-knowledge distillation (KD) training approach based on the teacher-student framework for knowledge distillation and knowledge transfer. As a deep neural network (DNN) considered in this paper, the deep residual network (ResNet), which is currently regarded as the latest DNN, is used for the teacher-student framework. Therefore, when implementing the Hint-KD training, we investigate the impact on the weight of KD information based on the soften factor in terms of classification accuracy using the widely used open deep learning frameworks, Caffe. As a results, it can be seen that the recognition accuracy of the student model is improved when the fixed value of the KD information is maintained rather than the gradual decrease of the KD information during training.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.1-17
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• 2012

The Smartphone, one of essential mobile devices widely used recently, can be very effectively applied to capture knowledge on the spot by jointly applying the pervasive functionality of cloud computing. The process of knowledge capturing can be also effectively automated if the topic of knowledge is automatically identified. Therefore, this paper suggests an interdisciplinary approach to automatically acquire knowledge on the spot by combining technologies of text mining-based topic identification and cloud computing-based Smartphone. The Smartphone is used not only as the recorder to record knowledge possessor's dialogue which plays the role of the knowledge source, but also as the sensor to collect knowledge possessor's context data which characterize specific situations surrounding him or her. The support vector machine, one of well-known outperforming text mining algorithms, is applied to extract the topic of knowledge. By relating the topic and context data, a business rule can be formulated, and by aggregating the rule, the topic, context data, and the dictated dialogue, a set of knowledge is automatically acquired.