• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.508-514
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• 2008

Knowledge management has been spread rapidly since Knowledge is regarded as a crucial asset in future. Once, Knowledge had been obtained freely but hard to be shared. Studies on importance and share of knowledge have been taking place and it leads to development of knowledge management systems. However, decision makers still have problems even though they are using knowledge management systems. Eventually, for the decision makers are needed concentrated knowledge which is unconscious and embodied, when they make a decision. This is wisdom. Thus, wisdom is superior to knowledge for the decision makers. There are some researches on wisdom but wisdom management were overlooked. This study presents differences between knowledge and wisdom with theoretical research and empirical analysis on the wisdom management.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.425-432
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• 2002

The purpose of this study is to help implement KISTI knowledge management systems through analysis of ordering which is most important among system characteristics. This study uses Delphi Technique to specialist in science and technology knowledge content area for empirical analysis. The result of this study is as followed First, the most important characteristic is retrieval function in systems. Second knowledge management systems basic characteristics like as retrieval function storage/retention function, accessibility, easy of usa classification function lank high position while additional characteristics like as knowledge linkage, knowledge evaluation, personalization, communication supporting, multimedia supporting lank low position.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.665-672
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• 2021

Humanity in the 21st century is ushering in an era of practical use of AI. Until now, even though the industrial structure has been advanced, mankind has seen that the abstraction of knowledge production is only their own domain, but they have doubts about that belief. Therefore, this paper tried to examine the identity of modern humanity from the perspective of the result of knowledge accumulated from the past. These discussions were summarized and presented in a historical model called "Changes in the way of accumulating knowledge step by step" starting from the emergence of the earth and mankind. The first stage of this analytical model is the "accumulation of DNA knowledge" until the emergence of human intelligence on Earth. The second stage is the process of "accumulating civilized knowledge" by human biological intelligence, which has become capable of producing knowledge on its own. It is currently classified into three stages and it is considered that it is entering the stage of "accumulating mechanical knowledge" using AI technology. This paper proposes human history as such a step-by-step knowledge accumulation model and describes related discussions.

• Journal of Korea Technology Innovation Society
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• v.14 no.1
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• pp.1-21
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• 2011

Technical change and technological innovation have become major drivers of economic progress in the knowledge oriented economies where growth, productivity, and competitiveness are increasingly based on improved technologies, novel products, upgraded processes or customized services. The creation of new knowledge, modifying or improving existent knowledge, or imitation of others, has become central to economic development. New discoveries, state-of-the-art information gathering procedures, or successful problem solving routines are often at he core of these innovations. Despite the generally acknowledged importance of science in many high-tech areas of major economic relevance, there is few science-related statistics to be found in high-profile international benchmarking reports. This paper aims to provide an answer by advancing our understanding of the possibilities of indicators quantifying linkages between science and technology. Central are the concepts of innovation capability and science/technology interface, which are used to assemble a wide range of empirical studies and quantitative indicators to summarize their possibilities and limitations for producing comparative statistics. For the purpose of the study, we extracted the US patents by Korean assignees or inventors, scientific papers cited in the patents in order to analyze the characteristics of linkage of scientific knowledge flows. The review focuses on indicators dealing with flows of written or codified information, and indicators of inventiveness that capture the non-codifiable tacit knowledge dimension. General conclusions will be drawn with a view towards further developments in the foreseeable future, suggesting new avenues for the design and implementation of patent-based and inventor-based relationships between scientific research and technical development within the context of regional or national systems of innovation.

• The Journal of Society for e-Business Studies
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• v.15 no.1
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• pp.173-189
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• 2010

This study empirically explores the relationship among knowledge management success factors (leadership, organizational culture, information technology, measurement, incentive, knowledge quality), knowledge management activities(knowledge creation and knowledge use), and task performance in disaster management area. Findings suggest that (1) organization culture, information technology, incentive and quality of knowledge are significantly related to knowledge creation activities while leadership, information technology, incentive and quality of knowledge are significantly related to knowledge use activities, (2) higher level of knowledge creation is related to high level of knowledge use, and (3) the level of knowledge use activities seems to be significantly related to task performance in disaster management, while knowledge creation activities are not.

• Journal of Korean Society of Archives and Records Management
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• v.1 no.2
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• pp.121-158
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• 2001

Development of R&D Knowledge Management System for Science & Technology Research Institutes, which was initiated early 2001, was introduced. The project, funded by the Ministry of Science and Technology, is being carried out by several R&D institutes in cooperation With implementers. The purposes of the project are: sharing knowledge about on-going research progresses and existing research results, facilitating cooperative efforts among research staff, teams, and institutes, and improving the efficiency of research project administration. The system consists of 4 components that are highly intertwined: Knowledge Portal, Knowledge Repository, Project Administration System and Groupware. This 3-year project will benefit R&D researchers, and R&D project carrying-out institutes and government project administration organization by facilitating research and development activities, and improving management efficiency, respectively.

• 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 the Korean Society for information Management
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• v.21 no.3
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• pp.31-44
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• 2004

Information center represents an important source which seems very well suited for knowledge management investigation. Information center is knowledge intensive and the use of advanced technology may transform the knowledge of business processes for delivering an unique capabilities in an organization. To exam knowledge management, organization culture, importance of knowledge, and support of information technology were identified as potential predictors of knowledge management support in information center. Three hypotheses have been tested with 43 database management specialist surveys. This study presents that the level of information sharing and knowledge exchange is significantly influenced by the extent support of information technology.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.1
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• pp.143-151
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• 2015

Technology convergence, recently accelerated in various technical fields, can be achieved by discovering a new technology while exchanging the knowledge among the different technologies and utilizing such knowledge into the existing fields of technology. In particular, technology convergence actively occurs in knowledge-intensive ICT. However, limited research is available on the routes of ICT technical knowledge diffusion because of insufficient data. Therefore, this study built a database on the citations of patent data applied from 2006 to 2013 in Korea and their cited patents. We drew a patent citation network, a technology citation network and an applicant citation network, after which we analyzed the routes of technical knowledge diffusion. Results showed that ICT played a leading role in knowledge citation among technologies and that such diffusion took a shorter time in technology citation when it occurred more frequently and when the citation occurred between ICTs. In addition, most of the ICT showed a strong citation relationship with the other ICTs or such technologies in the field of physics or electricity, whereas electric elements (H01) showed various citation relationships with technologies other than ICT. Furthermore, we found a strong technology diffusion relationship between domestic corporations and domestic natural persons. National organizations often cited the patents of other applicants, whereas the patents of domestic corporations were actively cited between domestic corporations or by other applicants. Thus, this study is expected to be useful in measuring the performance of technologies, including the diffusion to other technologies. As well as in considering the routes of technical knowledge diffusion in Korea.

• Information Systems Review
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• v.10 no.2
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• pp.179-194
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• 2008

To ensure continued existence, an organization must develop ways to share the knowledge that is possessed within the organization with the people who need, or who will need, that knowledge. Improving the efficiency of knowledge sharing is a highly desirable goal, but the issue of how best to motivate individuals to share their most valuable knowledge is not yet completely resolved. This paper aims to provide a sharing model on job related knowledge. Also, it intends to look for the factors that facilitate knowledge sharing among individuals in an organization. The research model is based on the technology acceptance model and it includes the perceived usefulness, perceived ease of use, attitude and intention to share constructs. Also, two external variables namely organizational culture and system quality were added. However, the actual use was excluded. In the research model, all hypotheses were found to be significant except one, which is the hypothesis that perceived usefulness will positively affect the intention to share.