• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2704-2719
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• 2022

Lately, neo-Chinese style furniture has been frequently noticed by product design professionals for the big part it played in promoting traditional Chinese culture. This article is an attempt to use big data semantic analysis method to provide effective design research method for neo-Chinese furniture design. By using big data mining program TEXTOM for big data collection and analysis, the data obtained from typical websites in a set time period will be sorted and analyzed. On the basis of "neo-Chinese furniture" samples, key data will be compared, classification analysis of overall data, and horizontal analysis of typical data will be performed by the methods of word frequency analysis, connection centrality analysis, and TF-IDF analysis. And we tried to summarize according to the related views and theories of the design. The research results show that the results of data analysis are close to the relevant definitions of design. The core high-frequency vocabulary obtained under data analysis, such as popular, furniture, modern, etc., can provide a reasonable and effective focus of attention for the designs. The result obtained through the systematic sorting and summary of the data can be a reliable guidance in the direction of our design. This research attempted to introduce related big data mining semantic analysis methods into the product design industry, to supply scientific and objective data and channels for studies on design, and to provide a case on the practical application of big data analysis in the industry.

• Cross-Cultural Studies
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• v.52
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• pp.341-369
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• 2018

Li ShangYin (李商隱) was a poet who represented the late Tang period and authored more than 590 poems. In this paper, I have searched for various phonetic and semantic relationships through the attention of scholars' annotation about Li ShangYin (李商隱)'s poetry. As a result, we found 12 types of "examples that explain the phonetic and semantic relationships of poetic words" and five types of "examples that explain the features of poetic words and prosody." Especially, through analysis of "examples that explain the phonetic and semantic relationships of poetic words", it is divided into two types. The first type is that the scholars' annotation about Li ShangYin (李商隱)'s poetry and phonetic and semantic relationships of poetic words are matched, and the second type is that the scholars' annotation about Li ShangYin (李商隱)'s poetry and phonetic and semantic relationships of poetic words are inconsistent. In this study, I applied the theory of level and oblique tones for more detailed analysis of each type.

• The Journal of Information Systems
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• v.21 no.3
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• pp.137-161
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• 2012

The increasing interest in big data analysis using various data mining techniques indicates that many commercial data mining tools now need to be equipped with fundamental text analysis modules. The most essential prerequisite for accurate analysis of text documents is an understanding of the exact semantics of each term in a document. The main difficulties in understanding the exact semantics of terms are mainly attributable to homonym and synonym problems, which is a traditional problem in the natural language processing field. Some major text mining tools provide a thesaurus to solve these problems, but a thesaurus cannot be used to resolve complex synonym problems. Furthermore, the use of a thesaurus is irrelevant to the issue of homonym problems and hence cannot solve them. In this paper, we propose a semantic text mining methodology that uses ontologies to improve the quality of text mining results by resolving the semantic ambiguity caused by homonym and synonym problems. We evaluate the practical applicability of the proposed methodology by performing a classification analysis to predict customer churn using real transactional data and Q&A articles from the "S" online shopping mall in Korea. The experiments revealed that the prediction model produced by our proposed semantic text mining method outperformed the model produced by traditional text mining in terms of prediction accuracy such as the response, captured response, and lift.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.3
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• pp.428-437
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• 2018

This study derives evaluation terms by analyzing the semantic relationship between design elements and sentiment terms in regards to fashion design. As for research methods, a total of 38,225 texts from Daum and Naver Blogs from November 2015 to October 2016 were collected to analyze the parts, frequency, centrality and semantic networks of the terms. As a result, design elements were derived in the form of a noun while fashion image and user's emotional responses were derived in the form of adjectives. The study selected 15 noun terms and 52 adjective terms as evaluation terms for men's striped shirts. The results of semantic network analysis also showed that the main contents of the users of men's striped shirts were derived as characteristics of expression, daily wear, formation, and function. In addition, design elements such as pattern, color, coordination, style, and fit were classified with evaluation results such as wide, bright, trendy, casual, and slim.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.73-82
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• 2015

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

• Journal of Industrial Technology
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• v.25 no.A
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• pp.123-131
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• 2005

Semantic object model has widely been recognized as an alternative data modeling approach to entity-relationship model for database system design. In this study, we have presented a semantic object model for intermediary type shopping mall consisting of multiple buyers and sellers. Essential processes and information with regard to the customer management, product management, price estimation, product order etc. have been considered for this study. Upon careful examination and analysis of them, a detailed semantic objects and attributes have been drawn and structured into semantic object diagrams. The final objects were converted into an entity-relationship diagram so that intuitive comparison could be made for relational database design. The results in this study may form a conceptual framework for both academic concerns and more complicated system applications.

• Korean Linguistics
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• v.80
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• pp.151-175
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• 2018

This paper proposed a method of semantic recognition to improve the extraction of correct answers of the Q&A system through machine learning. For this purpose, the semantic recognition method is described based on the distribution of predicative nouns. Predicative noun vocabularies and sentences were collected from Wikipedia documents. The predicative nouns are typed by analyzing the environment in which the predicative nouns appear in sentences. This paper proposes a semantic recognition method of predicative nouns to which rules can be applied. In Chapter 2, previous studies on predicative nouns were reviewed. Chapter 3 explains how predicative nouns are distributed. In this paper, every predicative nouns that can not be processed by rules are excluded, therefore, the predicative nouns noun forms combined with the case marker '의' were excluded. In Chapter 4, we extracted 728 sentences composed of 10,575 words from Wikipedia. A semantic analysis engine tool of ETRI was used and presented a predicative nouns noun that can be handled semantic recognition language.

• Journal of Korea Multimedia Society
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• v.13 no.11
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• pp.1586-1593
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• 2010

Vocabulary recognition error correction method has probabilistic pattern matting and dynamic pattern matting. In it's a sentences to based on key-word by semantic analysis. Therefore it has problem with key-word not semantic analysis for morphological changes shape. Recognition rate improve of vocabulary unrecognized reduced this paper is propose. In syllable restoration algorithm find out semantic of a phoneme recognized by a phoneme semantic analysis process. Using to sentences restoration that morphological analysis and morphological analysis. Find out error correction rate using phoneme likelihood and confidence for system parse. When vocabulary recognition perform error correction for error proved vocabulary. system performance comparison as a result of recognition improve represent 2.0% by method using error pattern learning and error pattern matting, vocabulary mean pattern base on method.

• Journal of the Korean Society of School Health
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• v.33 no.3
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• pp.184-193
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• 2020

Purpose: This study was designed to investigate the trends in school health research in Korea using probabilistic latent semantic analysis. The study longitudinally analyzed the abstracts of the papers published in 「The Journal of the Korean Society of School Health」 over the recent 17 years, which is between 2004 and August 2020. By classifying all the papers according to the topics identified through the analysis, it was possible to see how the distribution of the topics has changed over years. Based on the results, implications for school health research and educational uses of latent semantic analysis were suggested. Methods: This study investigated the research trends by longitudinally analyzing journal abstracts using latent dirichlet allocation (LDA), a type of LSA. The abstracts in 「The Journal of the Korean Society of School Health」 published from 2004 to August 2020 were used for the analysis. Results: A total of 34 latent topics were identified by LDA. Six topics, which were「Adolescent depression and suicide prevention」, 「Students' knowledge, attitudes, & behaviors」, 「Effective self-esteem program through depression interventions」, 「Factors of students' stress」, 「Intervention program to prevent adolescent risky behaviors」, and 「Sex education curriculum, and teacher」were most frequently covered by the journal. Each of them was dealt with in at least 20 papers. The topics related to 「Intervention program to prevent adolescent risky behaviors」, 「Effective self-esteem program through depression interventions」, and 「Preventive vaccination and factors of effective vaccination」 appeared repeatedly over the most recent 5 years. Conclusion: This study introduced an AI-powered analysis method that enables data-centered objective text analysis without human intervention. Based on the results, implications for school health research were presented, and various uses of latent semantic analysis (LSA) in educational research were suggested.

• Journal of IKEEE
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• v.12 no.4
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• pp.217-224
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• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.