Youngim Jung

Study on Strategies for Improving Application of Archived Data: Focused on International and Domestic Journal Articles

ABSTRACT Since core scholarly journals have been digitalized, demands on the perpetual access and long-term preservation of subscribed digital information resour ces by libraries are increasing. Various institutions and organizations have performed the prese rvation activities of digital scholarly resources for the purpose of responding those demands . This paper illustrates the National Digital Archive(NDA) system proposed and developed by KISTI and discusses on the NDA strategies which aim to improve the usability of archiv ed journal articles. In addition, NDA strategies of KISTI have been compared with those of intern ational researches and the economic validity of NDA has been verified by analyzing NPV , BCR and IRR. Legal system for improving the application of the archived data should be st udied next and high value-added data services have been suggested as our future studies in the final section.키워드: 항구 접근, 장기 보존, 국가 디지털 아카이브 , 학술논문정보, 보존 전략perpetual access, long-term preservation, national digital arch ive(NDA), journal articles, preservation strategy

STM E-Journal Use Analysis by Utilizing KESLI Usage Statistics Consolidation Platform

The provision of usage data for e-journals in standard format introduces an innovation in collection management practices within libraries, establishment of distribution policies for library consortia and frame-work of scholarly impact measurement. However, utilization of the usage statistics for consortia is disrupted because each publisher generates and transfers the large-scale usage reports in its own way. Thus we have implemented an automatic collection and visualization system of e-journal usage statistics in this paper. In addition, consortia package subscription data has been integrated into the suggested system. As the result, we have obtained 7,539,083 journal usage reports of 359 institutions from thirty-one publishers. By analyzing the usage statistics and cost, we have found out four major issues on use of STM e-journals distributed through KESLI consortia.

Korean scholarly information analysis based on Korea Science Citation Database (KSCD)

Research activities, quality and impact are measured for various reasons and citation analysis has been used as a tool in the assessment of scholarly impact. As to understand the influence of core scholarly work, searching and analyzing global citation databases such as Thomson Reuters Web of Science, Elseviers Scopus and Google Scholar is appropriate. However, those global citation databases have limitations in coverage and depth of non-English language journals. Thus this paper investigates the current trend of constructing national citation databases around Asia-Pacific regions and introduces Korea Science Citation-Index Database (KSCD). Citation analysis at the journal, article, reference and researcher affiliation level based on KSCD has been performed as to illustrate the overall picture of Korea Science research activities and impact as well.

Improving Efficiency of Usage Statistics Collection and Analysis in E-Journal Consortia

ABSTRACT The proliferating use of e-journals has led increasing interest in collecting and analyzing usage statistic information. However, the existing manual metho d and simple journal usage reports provided by publishers hinder the effective collection of large-scale usage statistics and the comprehensive/in-depth analysis on them. Thus we have proposed a hybrid automatic method of collecting e-journal usage statistics based on screen scraping and SUSHI protocol. In addition, the generation method of summary s tatistics presented in graphs, charts and tables has been suggested in this study. By utilizing the suggested system and analysis data, librarians can compose various report s on budget or operation of the libraries.키워드: 전자저널 이용통계, 스크린 스크래핑, SUSHI, COUNTER, 통계분석 e-journal usage statistics, screen scraping, SUSHI, COUNTER, st atistical analysis ****** 본 연구는 ‘핵심 과학기술 전자정보 공동활용 체계 구축 (K-12-L01-C01-S01)’ 사업의 일환으로 수행되었음 .한국과학기술정보연구원 해외정보실 선임연구원 (acorn@kisti.re.kr) ( 제1저자, 교신저자)한국과학기술정보연구원 해외정보실 책임연구원 (kimjh@kisti.re.kr) ( 공동저자)

Building korean classifier ontology based on korean wordnet

Being commonly used in most languages, the classifier must be reexamined using semantic classes from ontology However, few studies have dealt with the semantic categorization of classifiers and their semantic relations to nouns, which they quantify and characterize in building ontology In this paper, we propose the semantic recategorization of numeral classifiers in Korean and present the construction of a classifier ontology based on large corpora and KorLex 1.5 (Korean WordNet) As a result, a Korean classifier ontology containing semantic hierarchies and the relations of classifiers was constructed This is the first Korean classifier ontology, and its size is appropriate for natural language processing In addition, each of the individual classifiers has a connection to nouns or noun classes that are quantified by the classifiers.

Disambiguation based on wordnet for transliteration of arabic numerals for korean TTS

Transliteration of Arabic numerals is not easily resolved. Arabic numerals occur frequently in scientific and informative texts and deliver significant meanings. Since readings of Arabic numerals depend largely on their context, generating accurate pronunciation of Arabic numerals is one of the critical criteria in evaluating TTS systems. In this paper, (1) contextual, pattern, and arithmetic features are extracted from a transliterated corpus; (2) ambiguities of homographic classifiers are resolved based on the semantic relations in KorLex1.0 (Korean Lexico-Semantic Network); (3) a classification model for accurate and efficient transliteration of Arabic numerals is proposed in order to improve Korean TTS systems. The proposed model yields 97.3% accuracy, which is 9.5% higher than that of a customized Korean TTS system.

Hybrid Standard Platform for E-Journal Usage Statistics Management

The increasing availability of usage data for e-journals and the global standard project for usage statistics report has lead increasing interest on collecting and utilizing usage statistic information. However, collecting and integrating large-scale usage reports generated and transferred in a various way by publishers hinders librarians to utilize the usage statistics. Thus we have implemented an automatic collection and visualization system of e-journal usage statistics in this work. By using the suggested system, librarians can focus on more specific and concrete usage data of their institution. In addition, visualized statistics information can be utilized in the composition of various reports on budget or operation of the libraries.

Prediction of Korean Prosodic Phrase Boundary by Efficient Feature Selection in Machine Learning

Prediction of the prosodic phrase boundary is a potent influence on the performance of speech recognition and voice synthesis systems. We propose a statistical approach using efficient learning features for the natural prediction of the Korean prosodic phrase boundary. These new features reflect factors that affect the generation of the prosodic phrase boundary better than existing learning features. Notably, moreover, learning features that are extracted according to the hand-crafted prosodic phrase prediction rule impart higher accuracy. We evaluated the new learning features in terms of their efficiency in predicting the prosodic phrase boundary, using CRFs (Conditional Random Fields). The results were 84.63% accuracy for three levels and 80.14% accuracy for six levels.

Transliteration system for Arabic-Numeral Expressions using decision tree for intelligent Korean TTS

Though there has been much work on TTS technologies and several TTS systems have customized for Korean, current TTS systems output many errors in transliterating non-alphabetic symbols such as Arabic numerals and text symbols. Arabic Numeral Expressions (ANEs) show a high occurrence-frequency and deliver significant senses, especially in scientific or informative documents and texts. This paper proposes TAN (Transliteration system for Arabic-Numeral expressions) which can efficiently disambiguate the meaning and reading of Arabic Numeral Expressions in texts by using a decision tree. For the purpose of analyzing and learning data, three phases of learning elements were suggested: patterns of Arabic numerals combined with text symbols, contextual features and heuristic information were classified according to the senses and sounds of ANEs. Our corpus was made up of news articles issued from January 1st, 2000 to December 31SI, 2001 from 10 major newspapers in Korea. By learning the three phases of learning elements, the system shows 97.52% and 97.29% accuracies for the training set and the test set, respectively. This result shows that the accuracy of our system is 9.72% higher than that of a current TTS system for Korean.

Hybrid word sense disambiguation using language resources for transliteration of Arabic numerals in Korean

The high frequency of the use of Arabic numerals in informative texts and their multiple senses and readings deteriorate the accuracy of TTS systems. This paper presents a hybrid word sense disambiguation method exploiting a tagged corpus and a Korean wordnet, KorLex 1.0, for the correct and efficient conversion of Arabic numerals into Korean phonemes according to their senses. Individual contextual features are extracted from the tagged corpus and are grouped in order to determine the sense of Arabic numerals. Least upper bound synsets among common hypernyms of contextual features were obtained from the KorLex hierarchy, and they were used as semantic categories of the contextual features of Arabic numerals. The semantic classes were trained to classify the meaning and the reading of Arabic numerals using decision tree and to compose grapheme-to-phoneme rules for an automatic transliteration system for Arabic numerals. The proposed system outperforms the customized TTS systems by 3.9%--20.3%.