• Korean Journal of Heritage: History & Science
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• v.57 no.1
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• pp.54-79
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• 2024

The purpose of this research is to fill the vacuum created by the tendency of bias towards China among the curators of Korean museums who plan exhibitions focusing on Balhae, and to share with researchers in the countries concerned various supplementary research materials that could deepen their understanding of the history of Balhae. These materials are based on analyses of the details of exhibitions about Balhae held in a particular Russian museum and the characteristics of and changes in the museum's operational policy. Thus, this research focuses mainly on the permanent and special exhibitions held by the Far East History Museum and Reserve, whose collection represents the archaeological achievements of Russia regarding the history of Balhae. The first part of the research focuses on the layout of the exhibitions presented by the museum and the museum's operational policy. It reveals that the museum's permanent exhibitions follow a diachronic arrangement of the local history, while the first and second special exhibitions featured exhibits that were selected from the collections of the Russian Academy of Sciences and arranged according to specific themes. It also examines the museum's policy for operating the exhibitions, focusing on the operational rules, the human resources deployed to run them, and the related educational and PR programs. The second part of the research examines such issues as local politics, economy, education and culture related to the exhibitions on Balhae's history, and connects them to the background and development of the exhibitions. This study reveals that the permanent exhibitions were intended to promote historical awareness of the local area by museum visitors, particularly those who visited the exhibitions while the city was hosting important events such as international summits. It also reveals that the museum's first special exhibition led to the promotion of Korea-Russia cooperation on exchanges in the fields of culture and tourism, whereas the second special exhibition involved no PR efforts or related events, which was probably due to the changes that have occurred in the relationship between Russia and its neighboring countries since then. The final part of the study focuses on the characteristic features of the exhibition narratives, and compares school textbooks on local history and history books for general readers with the contents of the exhibitions. The analysis of the narratives based on the development of time shows that the history of the Mohe (or Malgal) tribes has been combined with that of Balhae, while they are treated separately in school textbooks. As regards political history, the narrative was largely focused on officials in Balhae's central government rather than on Mohe warriors in the border areas. The maps of Balhae presented in the exhibitions highlight the importance of accumulating empirical data. As for the exhibition of material cultures, this study suggests that the museums should obtain more archaeological floral and faunal remains related with agriculture and hunting. It also points out that the narrative on the theme of foreign relations deals with the archaeological relics of Unified Silla together with those of the Turkic tribes. As for the theme of philosophy and culture, the narrative focused on the state ceremonies and rituals of Goguryeo, a theme that has attracted little attention among Korean academic circles and which consequently requires further study. In conclusion, this study is meaningful in that it suggests a number of research topics regarding the development of exhibitions and exhibition narratives about the history of Balhae by a prestigious Russian museum that specializes in this subject.

• 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.