• The Korean Journal of Archival Studies
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• no.23
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• pp.3-43
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• 2010

Finding aids are tools which facilitate to locate and understand archives and records. Traditionally there are two types of archival finding aids: vertical and horizontal. Vertical finding aids such as inventories have multi-level descriptions based on provenance, while horizontal ones such as catalogs and index are tools to guide to the vertical finding aids based on the subject. In the web environment, traditional finding aids are evolving into more dynamic forms. Respecting the principles of provenance and original order, vertical finding aids are changing to multi-entity structures with development of ISAD(G), ISAAR(CPF) and ISDF as standards for describing each entity. However, vertical finding aids can be too difficult, complicated, and boring for many users, who are accustomed to the easy and exciting searching tools in the internet world. Complementing them, new types of finding aids are appearing to provide easy, interesting, and extensive access channels. This study investigates the development and limitation of vertical finding aids, and the recent trend of evolving new finding aids complementing the vertical ones. The study finds three new trends of finding aid development. They are (i) mixture, (ii) integration, and (iii) openness. In recent days, certain finding aids are mixed with stories and others provide integrated searches for the collections of various heritage institutions. There are cases for experimenting user participation in the development of finding aids using Web 2.0 applications. These new types of finding aids can also cause some problems such as decontextualised description and prejudices, especially in the case of mixed finding aids and quality control of user contributed annotations and comments. To solve these problems, the present paper suggests to strengthen the infrastructure of vertical finding aids and to connect them with various new ones and to facilitate interactions with users of finding aids. It is hoped that the present paper will provide impetus for archives including the National Archives of Korea to set up and evaluate the development strategies for archival finding aids.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.21-35
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• 2022

The recommended meal composition allows the general people to organize meals using the number of intakes of foods from each of six food groups (grains, meat·fish·eggs·beans, vegetables, fruits, milk·dairy products and oils·sugars) to meet Dietary Reference Intakes for Koreans (KDRIs) without calculating complex nutritional values. Through an integrated analysis of data from the 6th to 7th Korean National Health and Nutrition Examination Surveys (2013-2018), representative foods for each food group were selected, and the amounts of representative foods per person were derived based on energy. Based on the EER by age and gender from the KDRIs, a total of 12 kinds of diets were suggested by differentiating meal compositions by age (aged 1-2, 3-5, 6-11, 12-18, 19-64, 65-74 and ≥ 75 years) and gender. The 2020 Food Balance Wheel included the 6th food group of oils and sugars to raise public awareness and avoid confusion in the practical utilization of the model by industries or individuals in reducing the consistent increasing intakes of oils and sugars. To promote the everyday use of the Food Balance Wheel and recommended meal compositions among the general public, the poster of the Food Balance Wheel was created in five languages (Korean, English, Japanese, Vietnamese and Chinese) along with card news. A survey was conducted to provide a basis for categorizing nutritional problems by life cycles and developing customized web-based messages to the public. Based on survey results two types of card news were produced for the general public and youth. Additionally, the educational program was developed through a series of processes, such as prioritization of educational topics, setting educational goals for each stage, creation of a detailed educational system chart and teaching-learning plans for the development of educational materials and media.

• Journal of recreation and landscape
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• v.6 no.2
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• pp.63-71
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• 2012

As the interest in rural tourism has been increasing since the late 1990s, the research associated with rural tourism has increased, including research on the classification of rural tourism resources. The research classifying these resources has proved useful to many other studies. Although such studies have been conducted for a long time, they have expressed only experts' perspectives and been supported by statistics, without reflecting on users' opinions. Given this background, this study aims to classify rural tourism resources by focusing on the rural activities for tourists who use those tourism resources. To achieve this, each study participant proceeded to collect tourism resources by using a rural amenity resources database, and a card sorting test was conducted. Thirty-two people who had previously gone sightseeing in the rural areas were chosen as participants in the card sorting test. After the card sorting test was complete, the results were reviewed by experts. These results yielded six categories: doing nature activities, eating and cooking local dishes, putting up (overnight stays), going sightseeing/appreciating the landscape, enjoying leisure activities, and doing artistic activities. In the doing nature activities category, there were four subcategories: experiencing local resources, experiencing nature, experiencing tradition, and harvesting. This study was conducted to improve the satisfaction and understanding of the tourists who visit rural areas. Thus, the classification of rural tourism resources developed by this study will be widely used to establish the framework or contents of websites, applications, and so on, for promoting rural tourism resources and local resources.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.