• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.124-125
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• 2016

빅데이터 분석은 기존 데이터베이스 관리 도구로부터 데이터를 수집, 저장, 관리, 분석할 수 있는 역량을 말한다. 또한 대량의 정형 또는 비정형 데이터 집합으로부터 가치를 추출하고 결과를 분석하는 기술을 의미한다. 대부분의 빅데이터 분석 기술 방법들은 기존 통계학과 전산학에서 사용되던 데이터 마이닝, 기계 학습, 자연 언어 처리, 패턴 인식 등이 해당된다. 글로벌 리서치 기관들은 빅데이터를 2011년 이래로 최근 가장 주목받는 신기술로 지목해오고 있다. 따라서 대부분의 산업에서 기업들은 빅데이터의 적용을 통해 가치 창출을 위한 노력을 기울이고 있다. 본 연구에서는 다음 커뮤니케이션의 빅데이터 분석도구인 소셜 매트릭스를 활용하여 키워드 분석을 통해 콜라겐 키워드에 대한 의미를 분석하고자 한다. 또한 분석결과를 바탕으로 실무적 시사점을 제시하고자 한다.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1049-1062
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• 2004

With the popularization of a World Wide Web (WWW), the quantity of web information has been increased. Therefore, an efficient searching system is needed to offer the exact result of diverse Information to user. Due to this reason, it is important to extract and analysis of user requirements in the distributed information environment. The conventional searching method used the only keyword for the web searching. However, the searching method proposed in this paper adds the context information of keyword for the effective searching. In addition, this searching method extracts keywords by the new keyword extraction method proposed in this paper and it executes the web searching based on a keyword mining profile generated by the extracted keywords. Unlike the conventional searching method which searched for information by a representative word, this searching method proposed in this paper is much more efficient and exact. This is because this searching method proposed in this paper is searched by the example based query included content information as well as a representative word. Moreover, this searching method makes a domain keyword list in order to perform search quietly. The domain keyword is a representative word of a special domain. The performance of the proposed algorithm is analyzed by a series of experiments to identify its various characteristic.

• Journal of Information Science Theory and Practice
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• v.9 no.3
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• pp.76-106
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• 2021

The study analyzed bibliometric data of papers published in Korea Citation Index (KCI) and Web of Science (WoS) journals from 2002 to 2021. After examining size differences of KCI and WoS domains in the number of authors, institutions, and journals to put publication and citations counts in perspective, the study investigated co-authorship patterns over time to compare collaboration trends of Korean and international scholars and analyzed the data at author, institution, and journal levels to explore how the influences of authors, institutions, and journals on research output differ across domains. The study also conducted frequency-based analysis of keywords to identify key topics and visualized keyword clusters to examine topic trends. The result showed Korean LIS authors to be twice as productive as international authors but much less impactful and Korean institutions to be at comparable levels of productivity and impact in contrast to much of productivity and impact concentrated in top international institutions. Citations to journals exhibited initially increasing pattern followed by a decreasing trend though WoS journals showed far more variance than KCI journals. Co-authorship trends were much more pronounced among international publication, where larger collaboration groups suggested multi-disciplinary and complex nature of international LIS research. Keyword analysis found continuing diversification of topics in international research compared to relatively static topic trend in Korea. Keyword visualization showed WoS keyword clusters to be much denser and diverse than KCI clusters. In addition, key keyword clusters of WoS were quite different from each other unlike KCI clusters which were similar.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.4
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• pp.285-302
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• 2023

Based on this study, the research trends were identified for the field of public libraries in Korea by utilizing the keyword network analysis. For 20 years from 2003 to 2022, a total of 752 papers related to the public libraries published in the four largest academic journals in the field of library and information science in Korea were analyzed. The research results are as follows. First, from 2003 to 2022, an annual average of 37.6 papers were published, demonstrating a pattern of repeated rise and fall. Second, the keywords of 'service' and 'culture' were identified as the most discussed keywords as they were found to be among the top five in terms of the frequency of occurrence, connection centrality, and the mediation centrality analysis results. Third, in terms of the results of analyzing the co-occurrence frequency of keyword pairs, attention was paid to the keyword pairs of education-program, service-user, service-children, and service-disability.

• The Journal of Pediatrics of Korean Medicine
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• v.33 no.3
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• pp.42-55
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• 2019

Objectives The purpose of this study is to figure out the tendency of the commonly-used-pattern-identification and treatment for Hyperhidrosis by reviewing Korean clinical studies. Methods 18 articles which were published from August, 2004 to December, 2018. were obtained from the National discovery for science leader (NDSL), Research information sharing service (RISS), and Oriental medicine advanced searching integrated system (OASIS) by using keyword 'hyperhidrosis'. Results The most commonly-used-pattern-identification were the patterns with 'Heart' and 'Spleen-stomach'. Hyungbangsabaek-san and Taeeumjowi-tang were the most frequently used herbal medicine. The most common acupoints was LI4. The most common method of assessment was VAS. Conclusions This study identifies the most common pattern identification and treatment for hyperhidrosis. Developing systematic standards of pattern identification and treatment can be possible with further studies.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1150-1159
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• 2000

This paper presents an automatic text categorization model that improves the accuracy by combining statistical and knowledge-based categorization methods. In our model we apply knowledge-based method first, and then apply statistical method on the text which are not categorized by knowledge-based method. By using this combined method, we can improve the accuracy of categorization while categorize all the texts without failure. For statistical categorization, the vector model with Inverted Category Frequency (ICF) weighting is used. For knowledge-based categorization, Phrasal Patterns and Keyword Sets are introduced to represent sentence patterns, and then pattern matching is performed. Experimental results on new articles show that the accuracy of categorization can be improved by combining the tow different categorization methods.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.55-62
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• 2009

This paper proposes an improved method to verify a keyword segment that results from a word spotting system. First a baseline word spotting system is implemented. In order to improve performance of the word spotting systems, we use a two-pass structure which consists of a word spotting system and an utterance verification system. Using the basic likelihood ratio test (LRT) based utterance verification system to verify the keywords, there have been certain problems which lead to performance degradation. So, we propose a method which uses phone-level log-likelihood ratios (PLLR) patterns in computing confidence measures for each keyword. The proposed method generates weights according to the PLLR patterns and assigns different weights to each phone in the process of generating confidence measures for the keywords. This proposed method has shown to be more appropriate to word spotting systems and we can achieve improvement in final word spotting accuracy.

• Journal of Society of Preventive Korean Medicine
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• v.28 no.2
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• pp.19-29
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• 2024

Objective : Pattern identification in Korean Medicine is a core value, leading to a quantitative and qualitative increase in related studies. This study aims to identify trends in newly published research and discuss pattern identification. Method : We reviewed studies from 2015 to 2023 using the Oriental Medicine Advanced Searching Integrated System provided by the Korea Institute of Oriental Medicine. The keywords used were: pattern identification, cold-heat, deficiency-excess, exterior-interior, yin-yang, phlegm-retained fluid, static blood, qi-blood, qi deficiency, blood deficiency, yin deficiency, yang deficiency, and four-constitution. Results : A total of 150 studies met the inclusion criteria. These comprised 84 observational studies, including case reports, 2 experimental studies, and 64 literature reviews. Most studies were published in the Korean Journal of Oriental Physiology and Pathology. On average, 17.1 studies were published annually, although the number of studies has decreased over the years. Network analysis revealed that the main keyword in titles was pattern identification (n=122), followed by study (n=68) and patients (n=34). Conclusion : These findings highlight trends in studies related to pattern identification, with a focus on its standardization. Considering the limitations of pattern identification, a shift towards a disease-centered approach is recommended.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.209-219
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• 2004

In order to implement a real time hardware for keyword spotting, we propose a Single-End-Point DTW(SEP-DTW) algorithm which is simple and less complex for computation. The SEP-DTW algorithm only needs a single end point which enables efficient applications, and it has a small wont of computations because the global search area is divided into successive local search areas. Also, we adopt new local constraints and a new distance measure for a better performance of the SEP-DTW algorithm. Besides, we make a normalization of feature same vectors so that they have the same variance in each frequency bin, and each frame has the same energy levels. To construct several reference patterns for each keyword, we use a clustering algorithm for all training patterns, and mean vectors in every cluster are taken as reference patterns. In order to detect a key word for input streams of speech, we measure the distances between reference patterns and input pattern, and we make a decision whether the distances are smaller than a pre-defined threshold value. With isolated speech recognition and keyword spotting experiments, we verify that the proposed algorithm has a better performance than other methods.