• Journal of Korea Society of Industrial Information Systems
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• v.12 no.2
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• pp.49-59
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• 2007

This paper is about an system which can perform automatic semantic annotation to actualize "Semantic Web." Since it is impossible to tag numerous documents manually in the web, it is necessary to gather large Korean web documents as training data, and extract features by using natural language techniques and a thesaurus. After doing these, we constructed concept classifiers through the SVM (support vector machine) teaming algorithm. According to the characteristics of Korean language, morphological analysis and syntax analysis were used in this system to extract feature information. Based on these analyses, the concept code is mapped with Kadokawa thesaurus, which made it possible to map similar words and phrase to one concept code, to make training vectors. This contributed to rise the recall of our system. Results of the experiment show the system has a some possibility of semantic annotation.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Journal of Contemporary Eastern Asia
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• v.11 no.1
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• pp.1-20
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• 2012

This study analyzes messages from Korean TV debates on the conflict over U.S. beef imports and the process of negotiations over the imports in 2008. The authors have conducted a content analysis and a semantic network analysis by using KrKwic and CONCOR. The data was drawn from nine TV debates aired by three major TV networks in Korea (MBC, KBS, and SBS) from 27 April 27 2008 to 6 July 2008. The results indicate substantial differences in the semantic structure between arguments by the government and those by civic groups. We also investigated the relationship between the terms frequently used by both sides (i.e., the government and civic groups), and the terms used exclusively by one side. There was a gradual increase in the number of terms frequently used by both sides over time, from the formation of the conflict to its escalation to its resolution. The results indicate the possibility of general agreement in conflict situations.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.118-141
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• 2019

In South Korea, as awareness of gender equality increased since the 1990s, policies for gender equality and social awareness of equality have been established. Until recently, however, the gap between men and women in social and economic activities has not reached the globally desired level and led to social conflict throughout the country. In this study, we analyze the content of online news comments to understand the public perception of gender equality and the details of gender conflict and to grasp the emergence and diffusion process of emerging issues on gender equality. We collected text data from the online news that included the word 'gender equality' posted from January 2012 to June 2017 and also collected comments on each selected news item. Through text mining and the temporal semantic network analysis, we tracked the changes in discourse on gender equality and conflict. Results revealed that gender conflicts are increasing in the online media, and the focus of conflict is shifting from 'position and role inequality' to 'opportunity inequality'.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.611-616
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• 2020

In order to prevent the further spread of the COVID-19 virus in big cities, this paper proposes a semantic diagnosis and tracking system based on Linked Data through the cluster analysis of the infection situation in Seoul, South Korea. This paper is mainly composed of three sections, information of infected people in Seoul is collected for the cluster analysis, important infected patient attributes are extracted to establish a diagnostic model based on random forest, and a tracking system based on Linked Data is designed and implemented. Experimental results show that the accuracy of our diagnostic model is more than 80%. Moreover, our tracking system is more flexible and open than existing systems and supports semantic queries.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.2
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• pp.295-309
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• 2020

In 2019, Japan announced trade regulations against Korean products; consequently, the sales of Japanese products in Korea dropped due to a Korean consumers' boycott. This study measured the Korean consumers' political consumption behavior toward Japanese fashion products. Unstructured text data from online media sources and consumer posted sources such as blog and SNS were collected. Text mining techniques and semantic network analysis were used to process unstructured data. This study used text mining techniques and semantic network analysis to process data. The results identified boycotting Japanese fashion products and buycotting alternative products and Korean brands due to consumers' political consumption. Two brand cases were investigated in detail. Online text data before and after the political action were compared and significant changes in consumption as well as emotional expressions were identified. Product related industry sectors were identified in terms of the political consumption of fashion: liquor, automobile and tourism industry sectors were closely linked to the fashion sector in terms of boycotting. More "boycott" and "buycott" fashion brands (reflected in consumer attitudes and feelings) were detected in consumer driven texts than in media driven sources.

• Journal of Korean Elementary Science Education
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• v.37 no.1
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• pp.39-53
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• 2018

The purpose of this study was to investigate the conceptual structure of subjects related to matter having pre-service elementary school teachers by applying semantic network analysis (SNA). The analyzed concepts in the subjects of matter were 6 words such as 'atom', 'molecule', 'ion', 'electron', 'matter' and 'particle'. The results of SNA of the concepts are as follows : 1. In the semantic network of 'atom', words having a high betweenness centrality were linked with the words based on both the scientific context and the everyday context. 2. The network of 'molecule' was analyzed to be more organized than the network of the 'atom'. 3. In the network of 'ion', the group of words of the scientific context was distinguished from the group of words of the everyday context. 4. The network of 'electron' was analyzed to be more oriented on electricity and magnetism in the field of physics. 5. In the network of 'matter', the words related to compounds were linked with knowledge of history of science. 6. The network of 'particle' was not structured with words based on particulate nature of matter.

• Journal of Environmental Impact Assessment
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• v.8 no.1
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• pp.61-70
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• 1999

The purpose of this study is to understand an appreciation and a perceptive structure of "keijeok" image by Semantic Differential Technique and Factor Analysis. The data used in this study was obtained by the questionnaire survey carried out in Pusan metropolitan city. 15 adjective pairs in the survey were evaluated by the Semantic Differential scales graded 7 ranges from 1(very good) to 7(very bad). A total of 452 samples were collected by the survey and analyzed for this study. The results are as follows; First, 15 variables comprehended to "keijeok" image were estimated as a positive conception(LT 4.0). What's more, residents may perceive "keijeok" image as intangible and aesthetic aspect such as "fresh", "pleasant", "clean". Second, the result of factor analysis shows that factor I which express the major conceptual meaning of "keijeok" image tends to have intangible or aesthetic adjective pairs rather than concrete, whereas factor II which has the weaker meaning compared with factor I may represent a functional aspect of "keijeok" image. It can explain that the perceptive structure of "keijeok" image may be largely influenced by subjective sense, then added or concreted with objective conception or environmental situation. The results can be considered as an important matter which should be reflected at the stage of environmental planning for people's amiable and desirable place.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.129-137
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• 2018

The aim of this study is to analyze the President Directions Item using Semantic Network Analysis. This study has three contributions. First, this study shows the difference of policy directions through the frequency and contents of key words. Second, this study suggest patterns changes of decision-making of the president and bureaucracy through the key words network structure. Third, this study infers the interaction between the president's will and context of institutions.

• Korean Journal of Occupational Health Nursing
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• v.28 no.4
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• pp.221-229
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• 2019

Purpose: The aim of this study was to identify core keywords and topic groups of workplace bullying researches in the past 10 years for better understanding research trend. Methods: The study was conducted in four steps: 1) collecting abstracts, 2) extracting and cleaning semantic morphemes, 3) building co-occurrence matrix and 4) analyzing network features and clustering topic groups. Results: 437 articles between 2010 and 2019 were retrieved from 5 databases (RISS, NDSL, Google scholar, DBPIA and Kyobo Scholar). Forty-one abstracts from these articles were extracted, and network analysis was conducted using semantic network module. The most important core keywords were 'turnover', 'intention', 'factor', 'program' and 'nursing'. Four topic groups were identified from Korean databases. Major topics were 'turnover' and 'organization culture'. Conclusion: After reviewing previous research, it has been found that turnover intention has been emphasized. Further research focused on various intervention is needed to relieve workplace bullying in nursing field.