• International Journal of Railway
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• v.2 no.1
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• pp.37-42
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• 2009

Information management of products and services in every industries is gaining importance for resource planning and maintenance. In this paper, we analyzed the information classification systems for railway industry. International and domestic classification systems, such as HS, UNSPSC, eCl@ss and ISIC, are reviewed; as a result this paper presents the findings and the various issues. We proposed to-be images in adopting and utilizing the classification systems. Using the integrative information classification systems could make efficient electronic procurement, supply chain management and e-Business of railway services.

• Journal of Radiation Industry
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• v.8 no.3
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• pp.155-159
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• 2014

RI-Biomics is a new concept that combines radioisotopes (RI) and Biomics. For efficient collection of information, establishment of database for technical information system and its application to the system, there is an increasing need for constructing the standard classification system of technical information by its systematical classification. In this paper, we have summarized the development process of the standard classification system of technical information in the field of RI-Biomics and its application to the system. Constructing the draft version for the standard classification system of technical information was based on that standard classification one in national science and technology in Korea. The final classification system was then derived through the reconstruction and the feedback process based on the consultation from the 7 experts. These results were applied to the database of technical information system after transforming as standard code. Thus, the standard classification system were composed of 5 large classifications and 20 small classifications, and those classification are expected to establish the foundation of information system by achieving the circular structure of collection-analysis-application of information.

• Journal of Korea Technology Innovation Society
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• v.6 no.1
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• pp.110-124
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• 2003

In this study, we establish technology classification system for twelve material and component(MC) areas to sup-port systematic information services for MCT-20l0 which is supported by Korean government. We propose some design principles for MC technology classification system. The principles are suggested by considering of the characteristics of MC classification, regarding with scope, originality, hierarchy, relationship between technology classification and product classification, duplication and complex structure, use of information system, and life cycle of the classification system.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.450-455
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• 2002

Construction market situation has been changed quickly in the 21st century. One is a large variety of information, the other is the development of IT technology among the rest of the change. Accordingly, it is very important that information is managed systematically and made good use of broadly in proportion to the increase of information volume. Therefore, the purpose of this study is to propose a applicable classification system in the construction phase. First of all, the Construction Information Classification proposed by the government is studied to apply the actual work and to build a applicable construction information classification for construction project. A base of the applicable classification system is the Construction Information Classification, SMM and Materials classification(Public Procurement Service). The applicable classification system to control and manage the construction information is consist of the 4 types : Facility classification, Element classification, Work classification, Resource classification (Materials, Equipment, Labor).

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.39-45
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• 2022

For national industrial development in the Fourth Industrial Revolution, it is necessary to provide researchers with appropriate job information. This can be achieved by interconnecting the National Science and Technology Standard Classification System used for management of research activity with the Korean Employment Classification of Occupations used for job information management. In the present study, an automatic linkage model of classification systems is introduced based on a pre-trained language model for interconnecting science and technology information with job information. We propose for the first time an automatic model for linkage of classification systems. Our model effectively maps similar classes between the National Science & Technology Standard Classification System and Korean Employment Classification of Occupations. Moreover, the model increases interconnection performance by considering hierarchical features of classification systems. Experimental results show that precision and recall of the proposed model are about 0.82 and 0.84, respectively.

• International Journal of Contents
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• v.10 no.1
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• pp.43-46
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• 2014

Subject classification of journals is important because it can be utilized for the improvement of scholarly information services and analysis by research area. The classification by experts in a subject area wastes a lot of time and expense. On the other hand, the simple classification with basic information, such as the journal title has limitations. To solve this problem, this paper suggests the automatic classification of Korean journals using the SCI journals information cited by Korean journals, and an analysis of the classification result. In particular, this study adopted the WoS subject categories for classification to support the base for comparison between the Korean citation database and the global citation database (KSCI vs. SCI).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6080-6096
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• 2019

Text classification is one of the fundamental techniques in natural language processing. Numerous studies are based on text classification, such as news subject classification, question answering system classification, and movie review classification. Traditional text classification methods are used to extract features and then classify them. However, traditional methods are too complex to operate, and their accuracy is not sufficiently high. Recently, convolutional neural network (CNN) based one-hot method has been proposed in text classification to solve this problem. In this paper, we propose an improved method using CNN based skip-gram method for Chinese text classification and it conducts in Sogou news corpus. Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.

• International Journal of Knowledge Content Development & Technology
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• v.2 no.1
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• pp.51-65
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• 2012

While science and technology information service portals and heterogeneous databases produced in Korea and other countries are integrated, methods of connecting the unique classification systems applied to each database have been studied. Results of technologists' research, such as, journal articles, patent specifications, and research reports, are organically related to each other. In this case, if the most basic and meaningful classification systems are not connected, it is difficult to achieve interoperability of the information and thus not easy to implement meaningful science technology information services through information convergence. This study aims to address the aforementioned issue by analyzing mapping systems between classification systems in order to design a structure to connect a variety of classification systems used in the academic information database of the Korea Institute of Science and Technology Information, which provides science and technology information portal service. This study also aims to design a mapping system for the classification systems to be applied to actual science and technology information services and information management systems.

• Journal of Information Processing Systems
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• v.3 no.2
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• pp.73-81
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• 2007

In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naive Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.