• Fire Science and Engineering
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• v.23 no.2
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• pp.36-40
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• 2009

This paper presents research about the development of electrical fire cause classification in order to improve the reliability of electrical fire statistics and to collect electrical fires data efficiently. The incorrect and biased knowledge for electrical fires changed the classification of certain types of fires, from non-electrical to electrical. It is convenient and required to develop the standardized form that makes, in the assessment of the cause of electrical fires, the fire investigators directly ticking the appropriate box on the fire report form or making an assessment of a text description. In this study, newly developed electrical fire cause classification structure, which is well-defined hierarchical structure so that there are not any relationship or overlap between cause categories, is suggested. Also the suggested classification structure can be used for electrical fire investigation and statistics, which minimizes the mistake that diagnose non-electrical fires into electrical ones.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1-4
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• 2003

Knowledge classification and expression of constructed knowledge have been main research issues in the field of knowledge representation. Constructed design knowledge of the former product loses its utility when new products with different structures are introduced to the market. In order to construct the design knowledge for a new product. designers need to reconstruct the design knowledge with new relationships. The design knowledge has been constructed with level trees, but it is difficult to rearrange the relations. Design DNA is proposed in this work in order to facilitate the rearrangement of design knowledge and give flexibility to knowledge structure. Design DNA is based on Layout-oriented domain knowledge and Function-oriented domain knowledge, which enables to generate new design knowledge that will result in new part geometries for given constraints on the part functions. Design DNA is applied to the design knowledge of lever system of the automatic transmission of passenger cars as an example.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.3
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• pp.247-267
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• 2011

The purpose of this study is to investigate knowledge structure of knowledge organization research area in Korea. The study employed content analysis and network analysis to analyze degree centrality, betweenness, and eigenvector as well as frequency of words. It also analyzes research articles published during the period of 2000-2001. The study can be summarized that the network of keywords of knowledge organization researches is compact and complicated. Cataloging and classification play important roles in the network, and metadata and ontology becomes focal areas in knowledge organization. On the other hand, networks of authorships and authors are broad and fragmented. Collaboration is not active enough.

• International Journal of Safety
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• v.6 no.2
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• pp.17-21
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• 2007

This paper aims at the statistical analysis of electrical fire and classification of electrical fire causes to collect electrical fires data efficiently. Electrical fire statistics are produced to monitor the number and characteristics of fires attended by fire fighters, including the causes and effects of fire so that action can be taken to reduce the human and financial cost of fire. Electrical fires make up the majority of fires in Korea(including nearly 30% of total fires according to recent figures), The incorrect and biased knowledge for electrical fires changed the classification of certain types of fires, from non-electrical to electrical. It is convenient and required to develop the standardized form that makes, in the assessment of the cause of electrical fires, the fire fighters directly ticking the appropriate box on the fire report form or making an assessment of a text description. Therefore, it is highly recommended to develop electrical fire cause classification and electrical fire assessment on the fire statistics in order to categorize and assess electrical fires exactly. In this paper newly developed electrical fire cause classification structure, which is well-defined hierarchical structure so that there are not any relationship or overlap between cause categories, is suggested. Also fire statistics systems of foreign countries are introduced and compared.

• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.235-236
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• 2016

The importance of construction management has been increasing due to the fact that complex construction projects blend several different industries depending on the traits of the construction. This research was conducted to search for a method to enhance efficiency in cost management of construction project and meet the need for reusability of accumulated construction information. The process of detailed estimation and methodology for using standard unit price information has been developed to strengthen the interoperability in cost information by utilizing a standard classification system. The concept of ontology is proposed as a method of connecting construction information based on a standard breakdown structure to increasing the connectivity of the cost information in the construction project. Therefore, construction information knowledge framework is developed in order to improve the efficiency of the detailed estimation work process.

• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.75-86
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• 2002

In this study, we proposed design issues and structure of a web mining system and develop a system for the purpose of knowledge integration under world wide web environments resulted from our developing experiences. The developed system consists of three main functions： 1) gathering documents utilizing a search agent; 2) determining similarity coefficients between any two documents from term frequencies; 3) clustering documents based on similarity coefficients. It is believed that the developed system can be utilized for discovery of knowledge in relatively narrow domains such as news classification, index term generation in knowledge management.

• Journal of Sport and Applied Science
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• v.7 no.2
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• pp.53-60
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• 2023

Purpose: This study examines the role of leaders of sport organizations from the perspectives of rank-and-file volunteers. Specifically, the study explores which factors are important in leading volunteers and how rank-and-filers interact with their leaders. Research design, data, and methodology: This study reviews a comprehensive literature on volunteer and leadership theories which are trait theory, behavior theory, and contingency theory. Given the comprehension of prior structure of knowledge on leadership, the study provides a structure of knowledge on volunteer and leadership in sport context and discusses managerial implications for leaders in sport organization. Results: With an exploration of sport leadership, this study proposes a volunteer classification model which presents four-volunteer types: professional volunteer, company volunteer, general volunteer, and school volunteer. Furthermore, this study discussed managerial implications for sport organization leaders. Conclusions: Paid employees may be prepared to accept a job and its requirements mainly due to economic benefits. Volunteers, however, do not pursue economic benefits through their activity. Different types of motivation between paid employees and volunteers bring to surface how a leader influences volunteer effectively. A conceptual volunteer clarification model could be examined in real world situations. Insights for future studies were discussed.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.673-675
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• 2005

The art psychotherapy expert system is a computer system which helps to analyse one's psychology through pictures. However we need a standard criterion because the psychology, the target of the art psychotherapy, does not only have a ambiguous criterion but also a vast range. We're going to suggest a criterion in the field of the art psychotherapy by constructing systematic database through knowledge acquirement of the art psychotherapy expert system. In this study we introduce a system which enables systematic classification and confirmation of symptoms according to mental analyses. The suggested system enables confirmation of a classical structure and systematic classification of knowledges through conversation by extracting nouns through sentence analysis from the knowledge of descriptive form based on the clinical purpose of sentence analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.8
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• pp.1201-1212
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• 1993

This paper presents the automatically extracting method of data item from name-cards using knowledge-base. In our approach, we utilize a structural information and a relational information between data items and elements with knowledge in the name-cards. To describe a hierarchical knowledge, we uses a flame structure and we propose an algorithim of domain classification to extract item and group candidate domains from the name-cards. From the experimental results, we obtain the extraction rate, 95%, for 100 samples.