• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.63-77
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• 2011

The World Wide Web is a very large distributed digital information space. From its origins in 1991, the web has grown to encompass diverse information resources as personal home pasges, online digital libraries and virtual museums. Some estimates suggest that the web currently includes over 500 billion pages in the deep web. The ability to search and retrieve information from the web efficiently and effectively is an enabling technology for realizing its full potential. With powerful workstations and parallel processing technology, efficiency is not a bottleneck. In fact, some existing search tools sift through gigabyte.syze precompiled web indexes in a fraction of a second. But retrieval effectiveness is a different matter. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Furthermore, the most relevant documents do not nessarily appear at the top of the query output order. Also, current search tools can not retrieve the documents related with retrieved document from gigantic amount of documents. The most important problem for lots of current searching systems is to increase the quality of search. It means to provide related documents or decrease the number of unrelated documents as low as possible in the results of search. For this problem, CiteSeer proposed the ACI (Autonomous Citation Indexing) of the articles on the World Wide Web. A "citation index" indexes the links between articles that researchers make when they cite other articles. Citation indexes are very useful for a number of purposes, including literature search and analysis of the academic literature. For details of this work, references contained in academic articles are used to give credit to previous work in the literature and provide a link between the "citing" and "cited" articles. A citation index indexes the citations that an article makes, linking the articleswith the cited works. Citation indexes were originally designed mainly for information retrieval. The citation links allow navigating the literature in unique ways. Papers can be located independent of language, and words in thetitle, keywords or document. A citation index allows navigation backward in time (the list of cited articles) and forwardin time (which subsequent articles cite the current article?) But CiteSeer can not indexes the links between articles that researchers doesn't make. Because it indexes the links between articles that only researchers make when they cite other articles. Also, CiteSeer is not easy to scalability. Because CiteSeer can not indexes the links between articles that researchers doesn't make. All these problems make us orient for designing more effective search system. This paper shows a method that extracts subject and predicate per each sentence in documents. A document will be changed into the tabular form that extracted predicate checked value of possible subject and object. We make a hierarchical graph of a document using the table and then integrate graphs of documents. The graph of entire documents calculates the area of document as compared with integrated documents. We mark relation among the documents as compared with the area of documents. Also it proposes a method for structural integration of documents that retrieves documents from the graph. It makes that the user can find information easier. We compared the performance of the proposed approaches with lucene search engine using the formulas for ranking. As a result, the F.measure is about 60% and it is better as about 15%.

• Journal of Korean Library and Information Science Society
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• v.30 no.2
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• pp.123-153
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• 1999

This article aims at finding the ways, on the basis of the studies about the behaviors to search the existing CD-ROM databases, so that the searchers who retrieve the on-line MEDLINE used in the medical libraries can use the data more efficiently than now. We gave the questionnaires to the librarians in 60 medical libraries and searched the literatures and realities on the behaviors of the data uses to examine the search behaviors of the MEDLINE in the medical libraries. The result is as follows: 1) The medical data system rate for single users was $53\%$ and the ons for multi users $43\%$. As for the time which users retrieve for a week, under two hours was $75\%$, between 3 and 8 hours $18.3\%$, and eve. 9 hours $6.7\%$. 2) The increasing factors of the search result are (1) an enough discussion and interview between librarians and users, and (2) the use of the correct indexing terms, Thesaurus, and Keyword. In principle users must search directly. However, the librarians searched instead in case that the retrieval result was under two hours a week$(75\%)$. 3) As for the search fee, $91\%$ was free and $9\%$ was charged. Also search effectiveness was enhanced by the means of Inter-Library Loan Service & Information Network. 4) The medical librarians answered the questionnaire that they need the application education of professional knowledge, medical terms(thesaurus) and electronic medium, and also they need the computer education, interview technique and reeducation to give a satisfactory service. 5) As for the satisfactory degree of MEDLINE application, they answered $44.6\%$ for economy, $38.2\%$ for the conveniency of the time required, and $58.9\%$ for the users' search satisfaction answered respectively. 6) The application of MEDLINE system enhanced the medical libraries' image and had an effect on the users' satisfaction of using the data and search, the data activities and the research achievement. 7) In the past MeSH was used but as the time passes CD-ROM MEDLINE search behavior was preferred to On-line one.

• Journal of Distribution Science
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• v.12 no.6
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• pp.5-13
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• 2014

Purpose - The purposes of this editorial review are twofold: firstly, to introduce the four flagship international journals of the Korea Distribution Science Association(KODISA): the Journal of Distribution Science(JDS), the Journal of Industrial Distribution & Business(JIDB), the East Asian Journal of Business Management(EAJBM), and the Journal of Asian Finance, Economics and Business(JAFEB), and secondly, to identify the direction of the KODISA journals and the roles and responsibilities of the editors of the KODISA journals. Research design, data, and methodology - To achieve the goals, firstly, this review paper addresses the current progress of the four KODISA journals: JDS, JIDB, EAJBM, and JAFEB. Secondly, this paper defines the aims and missions of the four KODISA journals. JDS publishes the articles of examining past, current, and emerging trends and concerns in the area of distribution science and economics, logistics and SCM, transportation, distribution channel management, distribution innovation and information technology, merchandising and procurement, distribution and marketing, consumer behavior, and manufacturing, wholesaling, and retailing. JDS publishes both quantitative and qualitative research as well as scholarly commentaries, case studies, book reviews and other types of reports relating to all aspects of distribution. JIDB publishes the articles of examining past, current, and emerging trends and concerns in the areas of industry and corporate behavior, industry policy making, industrial distribution and business, e-commerce, and service industry. EAJBM publishes empirical and theoretical research papers as well as scholarly commentaries, case studies, book reviews, and other types of reports relating to all aspects of East Asian business and economy. JAFEB publishes original research analysis and inquiry into the contemporary issues of finance, economics and business management in Asia, including Central Asia, East Asia, South Asia, Southeast Asia, and Middle East. The mission of JAFEB is to bring together the latest theoretical and empirical finance, economics and business management research in Asian markets. The audiences of the KODISA journals include higher education institutions, scholars, industry researchers and practitioners, scientists, economists, and policy makers throughout the world. The main mission of the KODISA journals is to provide an intellectual platform for international scholars, promote interdisciplinary studies in social sciences and economics, and become leading journals in the social science and economics category in the world. Thirdly, this paper addresses the current status of indexing in major databases of the KODISA journals, namely: Cabell's Directories, EBSCO, SCOPUS (Elsevier), and Social Sciences Citation Index^® (SSCI, Thomson Reuters). Fourthly, this paper identifies the roles and responsibilities of the editors of the KODISA journals as the following: (1) Make sure that the journal be published in a timely manner and in international standards both in print and online versions. (2) Maintain the online homepage of the journal is always accessible to, and (3) Make sure that every article should go through a peer review process that meets international standards. Findings and conclusion - To accomplish the goals and missions of the KODISA journals, the editors of the KODISA journals must work together to publish high scholarly journals that meet international standards of journal publications.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.