• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.25-31
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• 2007

An automatic term-network construction system is proposed in this paper. This system uses the statistical values of the terms appeared in a document corpus. The 186 oral history documents collected from the Saemangeum area of Chollapuk-do, Korea, are used for the research. The term relationships presented in the term-network are decided by the cosine similarities of the term vectors. The number of the terms extracted from the documents is about 1700. The system is able to show the term relationships from the term-network as quickly as like a real-time system. The way of this term-network construction is expected as one of the methods to construct the ontology system and to support the semantic retrieval system in the near future.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.427-435
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• 2010

We extracted core terms by constructing scientific item networks from textbooks, analyzing their structures, and investigating the connected information and their relationships. For this research, we chose three high-school textbooks from different publishers for each three subjects, i.e, Science, Biology I and Biology II, to construct networks by linking scientific items in each sentence, where used items were regarded as nodes. Scientific item networks from all textbooks showed scare-free character. When core networks were established by applying k-core algorithm which is one of generally used methods for removing lesser weighted nodes and links from complex network, they showed the modular structure. Science textbooks formed four main modules of physics, chemistry, biology and earth science, while Biology I and Biology II textbooks revealed core networks composed of more detailed specific items in each field. These findings demonstrate the structural characteristics of networks in textbooks, and suggest core scientific items helpful for students' understanding of concept in Science and Biology.

• Journal of the Korean Society for information Management
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• v.5 no.1
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• pp.131-152
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• 1988

It is purposed in this paper to improve the retrieval cffect~venebs through the use of the seman-- t r knowledge of search terms in a computerbased search system. This study is developed it1 three stages include the experimentation of index terms or1 the probab~listir model, indexing with relational operators, and knowledgebase design. The sl~bject experimerltrd is the specific fklds of Chemical Engineering, ' Fluid Flow' and 'Combustion: As for the system ~rnplementatlon. two kinds of search method a r e done. Orie is to search terms related to one specialty word, the other is to retriele the articles based or1 the gueries.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.2
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• pp.353-378
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• 2014

The purpose of this study is to find factors to affect on the formation of semantic relation from terminology and what is to be affected by these factors to build the database scheme of terminology dictionary by a structural definition. In this research, 826,905 keywords of 88,874 social science articles and 985,580 keywords of 125,046 humanities science articles in the KCI journals from 2007 to 2011 were collected. From collected data, subject complexity, structural hole, term frequency, occurrence pattern and an effect between the number of nodes and the number of patterns which were derived from the semantic relation of linked terms of established 'STNet' System were analyzed. The summarized results from analyzed data and network patterns are as follows. Betweenness Centrality, term frequency, and effective size affect the numbers of semantic relation node. Among these factors, betweenness centrality was the most effective and effective size. But term frequency was the least effective. Betweenness Centrality, term frequency, and effective size affect the numbers of semantic relation type. Term frequency is the most effective. Therefore, when building a terminology dictionary, factors of betweenness centrality, term frequency, effective size, and complexity of subject are needed to select term. As a result, these factors can be expected to improve the quality of terminology dictionary.

• Journal of The Korean Association For Science Education
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• v.36 no.5
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• pp.791-800
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• 2016

This research aimed to identify core words used as associative words and definitions for expressing 'electricity' terminology and to find how core ones are activated to form a cognitive structure, using network analysis. The participants targeted 83 university freshmen students in the University of Education located in suburbs. Depending on their gender, whether or not they completed physics in high school, the associative words and definitions were analyzed using the network method, classifying two sections: before-lesson and after-lesson. The result is as follows: At before-lesson associative words for 'electricity' terminology, a slightly different network construction was revealed based on their two properties. However, after the class, they showed similar network structure irrespective of their distinctive characteristics. When it comes to other 'electricity' definitions, before taking the course, they had similar network connection across the gender but based on physics education status, there appeared subtle differences. Ultimately, after the class they demonstrated similar network structure regardless of their features. In conclusion, this paper suggests educational implications on network analysis, which covers 'electricity' terminology of university students.

• Review of KIISC
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• v.22 no.1
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• pp.7-13
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• 2012

최근까지 정보 기술 분야에서는 유비쿼터스(Ubiquitous)라는 용어가 크게 유행한 척이 있다. 현재는 다소 식상하게 들리는 용어일지도 모르지만 사물의 지능화와 통신화라는 유비쿼터스 개념은 이미 우리의 생활에 깊이 파고 들어왔다. 최근 유행하는 IT 기술인 스마트폰이나 지능형 전력망, 와이파이, 소셜 네트워크, 센싱 데이터 처리 기술과 개념은 이러한 유비쿼터스 기술의 대표적인 사례로 볼 수 있다. 국내에서 는 이러한 유비쿼터스라는 개념을 유비쿼터스 센서 네트워크(Ubiquitous Sensor Network: USN)라는 용어로서 2004년부터 정보통신부 혹은 지식경제부의 적극적인 지원에 힘입어 많은 기술적/산업적 성과를 이루기도 했다. 하지만 이러한 USN은 센서네트워크 개념으로 제한적으로 오용되는 경우가 많았다 이에 본고에서는 이러한 유비쿼터스 환경을 실현하는 사물의 지능화/통신화에 대한 기술 동향과 보안 기술을 논하기 위해서 USN과 거의 비슷한 개념이지만 국내외적으로 더욱 보편적으로 사용되고 있는 IoT(Intemet of Things) 용어를 사용할 것이며, 이러한 IoT에 대한 기술 동향과 보안 기술을 논하고자 한다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.3
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• pp.101-118
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• 2014

Given the context of rapid developing ubiquitous computing environment, it is imperative for users to search and use images based on affective meanings. However, it has been difficult to index affective meanings of image since emotions of image are substantially subjective and highly abstract. In addition, utilizing low level features of image for indexing affective meanings of image has been limited for high level concepts of image. To facilitate the access points of affective meanings of image, this study aims to utilize user-provided responses of images. For a data set, emotional words are collected and cleaned from twenty participants with a set of fifteen images, three images for each of basic emotions, love, sad, fear, anger, and happy. A total of 399 unique emotion words are revealed and 1,093 times appeared in this data set. Through co-word analysis and network analysis of emotional words from users' responses, this study demonstrates expanded word sets for five basic emotions. The expanded word sets are characterized with adjective expression and action/behavior expression.

• Information Systems Review
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• v.18 no.1
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• pp.141-155
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• 2016

This study identified terminologies related to the proximity and frequency of disaster by social network analysis (SNA) and text mining, and then expressed the outcome into a mind map. The termdocument matrix of text mining was utilized for the terminology proximity analysis, and the SNA closeness centrality was calculated to organically express the relationship of the terminologies through a mind map. By analyzing terminology proximity and selecting disaster response-related terminologies, this study identified the closest field among all the disaster response fields to disaster response and the core terms in each disaster response field. This disaster response terminology analysis could be utilized in future core term-based terminology standardization, disaster-related knowledge accumulation and research, and application of various response scenario compositions, among others.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.157-182
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• 2018

As data management and processing techniques have been developed rapidly in the era of big data, nowadays a lot of business companies and researchers have been interested in long tail data which were ignored in the past. This study proposes methods for generating and controlling a network of technical terms based on text mining technique to enhance data utilization in the distribution of long tail theory. Especially, an edit distance technique of text mining has given us efficient methods to automatically create an interlinking network of technical terms in the scholarly field. We have also used linked open data system to gather experimental data to improve data utilization and proposed effective methods to use data of LOD systems and algorithm to recognize patterns of terms. Finally, the performance evaluation test of the network of technical terms has shown that the proposed methods were useful to enhance the rate of data utilization.