• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.83-103
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• 2015

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining-extracting meaningful information from unstructured text data-has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.71-95
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• 2016

In recent years, text mining has been used to extract meaningful insights from the large volume of unstructured text data sets of various domains. As one of the most representative text mining applications, topic modeling has been widely used to extract main topics in the form of a set of keywords extracted from a large collection of documents. In general, topic modeling is performed according to the weighted frequency of words in a document corpus. However, general topic modeling cannot discover the relation between documents if the documents share only a few terms, although the documents are in fact strongly related from a particular perspective. For instance, a document about "sexual offense" and another document about "silver industry for aged persons" might not be classified into the same topic because they may not share many key terms. However, these two documents can be strongly related from the R&D perspective because some technologies, such as "RF Tag," "CCTV," and "Heart Rate Sensor," are core components of both "sexual offense" and "silver industry." Thus, in this study, we attempted to discover the differences between the results of general topic modeling and R&D perspective topic modeling. Furthermore, we package social issues from the R&D perspective and present a prototype system, which provides a package of news articles for each R&D issue. Finally, we analyze the quality of R&D perspective topic modeling and provide the results of inter- and intra-topic analysis.

• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.231-257
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• 2013

The recent rise in the unstructured data generated by social media has resulted in an increasing need to collect, store, search, analyze, and visualize it. These data cannot be managed effectively by using traditional data analysis methodologies because of their vast volume and unstructured nature. Therefore, many attempts are being made to analyze these unstructured data (e.g., text files and log files) by using commercial and noncommercial analytical tools. Especially, the attempt to discover meaningful knowledge by using text mining is being made in business and other areas such as politics, economics, and cultural studies. For instance, several studies have examined pending national issues by analyzing large volumes of texts on various social issues. However, it is difficult to create satisfactory information services that can identify R&D documents on specific national issues from among the various R&D resources. In other words, although users specify some words related to pending national issues as search keywords, they usually fail to retrieve the R&D information they are looking for. This is usually because of the discrepancy between the terms defining pending national issues and the corresponding terms used in R&D documents. We need a mediating logic to overcome this discrep 'ancy so that we can identify and package appropriate R&D information on specific pending national issues. In this paper, we use association analysis and social network analysis to devise a mediator for bridging the gap between the keywords defining pending national issues and those used in R&D documents. Further, we propose a methodology for packaging R&D information services for pending national issues by using the devised mediator. Finally, in order to evaluate the practical applicability of the proposed methodology, we apply it to the NTIS(National Science & Technology Information Service) system, and summarize the results in the case study section.

• BMB Reports
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• v.56 no.11
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• pp.594-599
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• 2023

A number of therapeutic drugs have been developed from functional chemicals found in plants. Knowledge of plants used for medicinal purposes has historically been transmitted by word of mouth or through literature. The aim of the present study is to provide a systemic platform for the development of lead compounds against breast cancer based on a traditional medical text. To verify our systematic approach, integrating processes consisted of text mining of traditional medical texts, 3-D virtual docking screening, and in vitro and in vivo experimental validations were demonstrated. Our text analysis system identified rutin as a specific phytochemical traditionally used for cancer treatment. 3-D virtual screening predicted that rutin could block EGFR signaling. Thus, we validated significant anti-cancer effects of rutin against breast cancer cells through blockade of EGFR signaling pathway in vitro. We also demonstrated in vivo anti-cancer effects of rutin using the breast cancer recurrence in vivo models. In summary, our innovative approach might be proper for discovering new phytochemical lead compounds designing for blockade of malignant neoplasm including breast cancer.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.346-351
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• 2023

With the rapid development of Artificial Intelligence (AI) technology, there is an increasing variety of methods for creating 3D models. These include innovations such as text-only generation, 2D images to 3D models, and combining images with cue words. Each of these methods has unique advantages, opening up new possibilities in the field of 3D modeling. The purpose of this study is to explore and summarize these methods in-depth, providing researchers and practitioners with a comprehensive perspective to understand the potential value of these methods in practical applications. Through a comprehensive analysis of pure text generation, 2D images to 3D models, and images with cue words, we will reveal the advantages and disadvantages of the various methods, as well as their applicability in different scenarios. Ultimately, this study aims to provide a useful reference for the future direction of AI modeling and to promote the innovation and progress of 3D model generation technology.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.317-324
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• 2012

Recently, coordination task of similarity between national R&D programs is emphasized on view from the R&D investment efficiency. But the previous similarity search method like text-based similarity search which using keyword of R&D projects has reached the limit due to deviation of document's quality. For the solve the limitations of text-based similarity search using the keyword extraction, in this study, utilization of R&D project's technical classification will be discussed as a new similarity search method when analyzed of similarity between national R&D programs. To this end, extracts the Science and Technology Standard Classification of R & D projects which are collected when national R&D Survey & analysis, and creates peculiar vector model of each R&D programs. Verify a reliability of this study by calculate the cosine-based and Euclidean distance-based similarity and compare with calculated the text-based similarity.

• Smart Media Journal
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• v.4 no.3
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• pp.44-49
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• 2015

Text line segmentation is a preprocessing step in OCR, which can significantly influence the accuracy of document analysis applications. This paper proposes a novel methodology for the text line segmentation of handwritten documents. First, the average width of the connected components is used to form a 1-D Gaussian kernel and a smoothing operation is then applied to the input binary image. The adaptive binarization of the smoothed image forms the final text lines. In this work, the segmentation method involves two stages: firstly, the large connected components are labelled as a unique text line using text line area mapping. Secondly, the final refinement of the segmentation is performed using the Euclidean distance between the text line and small connected components. The group of uniquely labelled text candidates achieves promising segmentation results. The proposed approach works well on Korean and English language handwritten documents captured using a camera.

• Proceedings of the Zoological Society Korea Conference
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• 1997.04a
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• pp.47-47
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• 1997

No Abstract, See Full Text

• Journal of Microbiology and Biotechnology
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• v.27 no.8
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• pp.1392-1400
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• 2017

Galactooligosaccharides (GOSs) are known to be selectively utilized by Bifidobacterium, which can bring about healthy changes of the composition of intestinal microflora. In this study, ${\beta}-GOS$ were synthesized using bifidobacterial ${\beta}-galactosidase$ (G1) purified from recombinant E. coli with a high GOS yield and with high productivity and enhanced bifidogenic activity. The purified recombinant G1 showed maximum production of ${\beta}-GOSs$ at pH 8.5 and $45^{\circ}C$. A matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis of the major peaks of the produced ${\beta}-GOSs$ showed MW of 527 and 689, indicating the synthesis of ${\beta}-GOSs$ at degrees of polymerization (DP) of 3 and DP4, respectively. The trisaccharides were identified as ${\beta}-{\text\tiny{D}}$-galactopyranosyl-($1{\rightarrow}4$)-O-${\beta}-{\text\tiny{D}}$-galactopyranosyl-($1{\rightarrow}4$)-O-${\beta}-{\text\tiny{D}}$-glucopyranose, and the tetrasaccharides were identified as ${\beta}-{\text\tiny{D}}$-galactopyranosyl-($1{\rightarrow}4$)-O-${\beta}-{\text\tiny{D}}$-galactopyranosyl-($1{\rightarrow}4$)-O-${\beta}-{\text\tiny{D}}$-galactopyranosyl-($1{\rightarrow}4$)-O-${\beta}-{\text\tiny{D}}$-glucopyranose. The maximal production yield of GOSs was as high as 25.3% (w/v) using purified recombinant ${\beta}-galactosidase$ and 36% (w/v) of lactose as a substrate at pH 8.5 and $45^{\circ}C$. After 140 min of the reaction under this condition, 268.3 g/l of GOSs was obtained. With regard to the prebiotic effect, all of the tested Bifidobacterium except for B. breve grew well in BHI medium containing ${\beta}-GOS$ as a sole carbon source, whereas lactobacilli and Streptococcus thermophilus scarcely grew in the same medium. Only Bacteroides fragilis, Clostridium ramosum, and Enterobacter cloacae among the 17 pathogens tested grew in BHI medium containing ${\beta}-GOS$ as a sole carbon source; the remaining pathogens did not grow in the same medium. Consequently, the ${\beta}-GOS$ are expected to contribute to the beneficial change of intestinal microbial flora.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.619-630
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• 2015

The purpose of this study is to provide basic information to develop a game strategy plan of a team in a future by identifying the patterns of attack and pass of national men's professional volleyball teams and extracting core key words related with volleyball game performance to evaluate game performance using 'social network analysis' and 'text mining'. As for the analysis result of 'social network analysis' with the whole data, group '0' (6 players) and group '1' (11 players) were partitioned. A point of view the degree centrality and betweenness centrality in 'social network analysis' results, we can know that the group '1' more active game performance than the group '0'. The significant result for two group (win and loss) obtained by 'text mining' according to two groups ('0' and '1') obtained by 'social network analysis' showed significant difference (p-value: 0.001). As for clustering of each network, group '0' had the tendency to score points through set player D and E. In group '1', the player K had the tendency to fail if he attack through 'dig'; players C and D have a good performance through 'set' play.