• The Research Journal of the Costume Culture
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• v.3 no.1
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• pp.17-25
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• 1995

The aim of thesis is to analyze dress phenomena, the semantic function and meaning of clothing were respectively on the basis of Semantics and Society by Geoffrey Leech and mentalistic semantics. To comprehend the actual clothing behaviour better, the pictures taken on the streets were used, including all kinds of the western-style and the traditional Korean costumes in Korea. The followings are the findings of the analysis. A in language, the semantic functions of the clothing are the informational, the expressive, the directive, the aesthetic, and the phatic functions. They communicate operating simultaneously. The clothing is the mentalistic semantics.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.105-115
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• 2018

The descriptive-type question is appropriate for deep thinking ability evaluation, but it is not easy to grade. Since, even though same grading criterion, the graders produce different scores, we need the objective evaluation system. However, the system needs the Korean analysis. As the descriptive-type answering is described with the compound sentence, the system has to analyze the compound sentence. This paper develops the Korean syntactic-semantic analysis system for compound sentence and evaluates performance of the system. This system selects the modifiee of the word phrase using syntactic-semantic constraint and semantic dictionary. The 93% accurate rate shows that the system is effective. This system will be utilized in descriptive-type grading and Korean processing.

• Journal of Korean Elementary Science Education
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• v.32 no.3
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• pp.327-345
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• 2013

This study aims to investigate how pre-service elementary school teachers understand 'something scientific', 'being scientific', 'scientific events' and 'scientific questions' through semantic network analysis. To achieve this purpose, this study carried out a central analysis of the frequency and density of words and the degree of connection between key words, a concentric analysis, a click analysis and a common network analysis through text semantic network analysis by using NetMiner 4.0 Program. Based on the results of these analyses, this study came to the following conclusions. Firstly, in perceiving 'something scientific', pre-service elementary school teachers recognized 'verification', 'objective' and 'experiment' as most important words. In other words, they perceived that main grounds for something scientific should be provided through clear facts, possible to be verified and accompanied by an exact and logical theoretical system. In regard to 'being scientific', they perceived 'explanation', 'objective' and 'verification' as most important words, while having a traditional point of view that science is a set that can be explained objectively. Secondly, in regard that the term, 'observation', is contained in 'scientific events', they showed a high rate of understanding it as a scientific event. In regard to scientifical reasons, they showed the highest frequency of 'observation', and for unscientific reasons, they showed the highest frequency of 'behavior'. In perceiving 'scientific questions', they showed the highest frequency of determining bacteria-related questions as scientific. As a reason why they thought as scientific, they mentioned 'observation' most frequently like 'scientific events', while mentioning 'value judgement' as a reason why they thought as unscientific most frequently. From the results of integrated network analysis, this study found out that words pre-service teachers commonly used in stating scientific events or scientific questions were overlapped with words they mentioned for scientific events or scientific questions. As a result, it was found there were many pre-service teachers having interpreted scientific words without clearly distinguishing scientific events or scientific questions.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.2
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• pp.67-79
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• 1998

Inthe study, a method to evaluate the door closing sound has been developed. Based on the factor analysis, various adjective pairs which describe meaning of the door impact sound have been differentiated. This approach, called Semantic difference(SD) method, was originally developed in linguistics research on order to compare diverse mother tongue. This paper introduces at first how the door sound os generated and transmitted. After that, a factor analysis which is a tool of SD method is implemented to door closing sound for 12 domestic and 1 foreign car models. During investigation, the examined models are categorized into small, medium and luxurious size automobiles. The adjective pairs which attritbute to the door quality have been factorized into three group : expensive/ smooth, powerful/ heavy, and modern/dull. It turns out that the first factor : expensive/ smooth plays the most important role in door closing sound quality.

• Korean Journal of Oriental Medicine
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• v.11 no.2
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• pp.35-52
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• 2005

This study is to design a Semantic Navigation Retrieval System for Oriental Medicine Articles based on a XTM so that people can search and use them more effectively than before. Keywords extracted from articles are categorized 4 topics : herbs, prescription, disease, and action. Keywords analysis Ontology is modeled based on 4 topics and their relations, and then represented Topic maps. Next, Article analysis Ontology is consist of title, author, keywords, abstracts and organization Topics from metadata. Keywords and Article analysis Ontology were integrated through Keywords Topic. Korean Medical Article Retrieval System is optimistic in terms on search results supporting semantic navigation in the information service aspects and easier accessibility because all related information are semantically connected with each different DBs.

• Journal of Environmental Science International
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• v.21 no.9
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• pp.1053-1058
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• 2012

In this study, soundscape was analyzed through physical measurement and social surveys. The results showed that, soundscape components were related to functional partitions and tourist activities, at the same time influenced by ambient noise. The sound pressure levels showed daily regular changes. Semantic differential analysis showed that the evaluation of the soundscape in urban park was rather complicated. It was still possible to identify major factors including relaxation, spatiality and environment. The results provided theoretical basis for improving urban park soundscape, and called attention to this problem.

• Journal of IKEEE
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• v.13 no.2
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• pp.241-247
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• 2009

This paper suggests the expansion methods of semantic dictionary, taking Korean semantic features account. These methods will be used to extract a practical topic word in the automatic summarization. The first is the method which is constructed the synonym dictionary for improving the performance of semantic-marker analysis. The second is the method which is extracted the probabilistic information from the subcategorization dictionary for resolving the syntactic and semantic ambiguity. The third is the method which is predicted the subcategorization patterns of the unregistered predicate, for the resolution of an affix-derived predicate.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.129-148
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• 2016

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.112-122
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• 2007

Training a statistical model for semantic role labeling requires a large amount of manually tagged corpus. However. such corpus does not exist for Korean and constructing one from scratch is a very long and tedious job. This paper suggests a modified algorithm of self-training, an unsupervised algorithm, which trains a semantic role labeling model from any raw corpora. For initial training, a small tagged corpus is automatically constructed iron case frames in Sejong Electronic Dictionary. Using the corpus, a probabilistic model is trained incrementally, which achieves 83.00% of accuracy in 4 selected adverbial cases.