• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.22 no.1
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• pp.135-166
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• 1977

Some biochemical features of varietal variation in seed protein and their implications for soybean breeding for high protein were pursued employing 86 soybean varieties of Korea, Japan, and the U.S.A. origins. Also, studied comparatively was the temporal pattern of protein components accumulation during seed development characteristic to the high protein variety. Seed protein content of the 86 soybean varieties varied 34.4 to 50.6%. Non-existence of variety having high content of both protein and oil, or high protein content with average oil content as well as high negative correlation between the content of protein and oil (r=-0.73$^{**}$) indicate strongly a great difficulty to breed high protein variety while conserving oil content. The total content of essential amino acids varied 32.82 to 36.63% and the total content of sulfur-containing amino acids varied 2.09 to 2.73% as tested for 12 varieties differing protein content from 40.0 to 50.6%. The content of methionine was positively correlated with the content of glutamic acid, which was the major amino acid (18.5%) in seed protein of soybean. In particular, the varieties Bongeui and Saikai ＃20 had high protein content as well as high content of sulfur-containing amino acids. The content of lysine was negatively correlated with that of isoleucine, but positively correlated with protein content. The content of alanine, valine or leucine was correlated positively with oil content. The seed protein of soybean was built with 12 to 16 components depending on variety as revealed on disc acrylamide gel electrophoresis. The 86 varieties were classified into 11 groups of characteristic electrophoretic pattern. The protein component of Rm=0.14(b) showed the greatest varietal variation among the components in their relative contents, and negative correlation with the content of the other components, while the protein component of Rm=0.06(a) had a significant, positive correlation with protein content. There was sequential phases of rapid decrease, slow increase and stay in the protein content during seed development. Shorter period and lower rate of decrease followed by longer period and higher rate of increase in protein content during seed development was of characteristic to high protein variety together with earlier and continuous development at higher rate of the protein component a. Considering the extremely low methionine content of the protein component a, breeding for high protein content may result in lower quality of soybean protein.n.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• Radiation Oncology Journal
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• v.16 no.4
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• pp.433-440
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• 1998

Purpose : Although small ceil lung cancer (SCLC) has high response rate to chemotherapy and radiotherapy (RT), the prognosis is dismal. The authors evaluated survival and failure patterns according to the prognostic factors in SCLC patients who had thoracic radiation therapy with chemotherapy. Materials and Methods : One hundred and twenty nine patients with SCLC had received thoracic radiation therapy from August 1985 to December 1990. Seventy-seven accessible patients were evaluated retrospectively among 87 patients who completed RT. Median follow-up period was 14 months (2-87months). Results : The two years survival rate was 13$\%$ with a median survival time of 14 months. The two year survival rates of limited disease and extensive disease were 20$\%$ and 8$\%$, respectively, with median survival time of 14 months and 9 months, respectively. Twenty two patients (88$\%$) of limited disease showed complete response (CR) and 3 patients (12$\%$) did partial response (PR). The two year survival rates on CR and PR groups were 24$\%$ and 0$\%$, with median survival times of 14 months and 5 months. respectively (p=0.005). No patients with serum sodium were lower than 135 mmol/L survived 2years and their median survival time was 7 months (p=0.002). Patients whose alkaline phophatase lower than 130 IU/L showed 26$\%$ of 2 year survival rate and showed median survival time of 14 months and those with alkaline phosphatase higher than 130 IU/L showed no 2 year survival and median survival time of 5 the months, respectively (p=0.019). No statistical differences were found according to the age, sex, and performance status. Among the patients with extensive disease, two rear survivals according to the metastatic sites were 14$\%$, 0$\%$, and 7$\%$ in brain, liver, and other metastatic sites, respectively, with median survival time of 9 months, 9 months, and 8 months, respectively (p>0.05). Two year survivals on CR group and PR group were 15$\%$ and 4$\%$, respectively, with a median survival time of 11 months and 7 months, respectively (p=0.01). Conclusion : For SCLC, complete response after chemoradiotherapy was the most significant prognostic tactor. To achieve this goal. there should be further investigation about hyperfractionation, dose escalation, and compatible chemo-radiation schedule such as concurrent chemo-radiation and early radiation therapy with chemotherapy.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.698-708
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• 2018

The purpose of this study was to investigate differences in the breeding call characteristics of cicada species found in urban and forest areas in the central region of Korea by examining the interspecific effects and environmental factors affecting the breeding calls and breeding call patterns. The selected research sites were Gyungnam Apartment in Bangbae-dong, Seoul for the urban area and Chiak Mountain National Park in Wonju for the forest area. The research method for both sites was to record cicada breeding calls for 24 hours with a recorder installed at the site and analyze the results. Data from the Korea Meteorological Administration were used for environmental factors. The research period was from June 19, 2017 to September 30, 2017. As a result of the study, there were differences in the emergence of species between the two research sites: while Platypleura kaempferi, Hyalessa fuscata, Meimuna opalifera, Graptopsaltria nigrofuscata, and Suisha coreana were observed at both sites, Cryptotympana atrata was observed in the urban area and Leptosemia takanonis in the forest area only. The emergence periods of cicadas at the two sites were also different. The activities of P. kaempferi and L. takanonis were noticeable in the forest area. In the urban area, however, L. takanonis was not observed and the duration of activity of P. kaempferi was short. In the urban area, C. atrata appeared and sang for a long period; H. fuscata, M. opalifera, and G. nigrofuscata appeared earlier than in the forest area. S. coreana appeared earlier in the forest area than in the urban area. According to the daily call cycle analysis, even cospecific cicada showed a wide variation in their daily cycle depending on the region and the interspecific effects between different cicadas, and the environmental differences between the urban and forest areas affected the calls of cicadas. The results of correlation analysis between each cicada breeding calls and environmental factors of each site showed positive correlation with average temperature of most cicadas except P. kaempferi and C. atrata. The same species of each site showed positive correlations with more diverse weather factors such as solar irradiance. Logistic regression analysis showed that cicadas with overlapping calling times had significant effects on each other's breeding calls. C. atrata, which appeared only in the urban area, had a positive effect on the calling frequency of H. fuscata, M. opalifera, and G. nigrofuscata, which called in the same period. Additionally, L. takanonis, which appeared only in the forest area, and P. kaempferi had a positive effect on each other, and M. opalifera had a positive effect on the calling frequency of H. fuscata and G. nigrofuscata in the forest area. For the environmental factors, the calling frequency of cicadas was affected by the average temperatures of the urban and forest areas, and cicadas that appeared in the forest area were also affected by the amount of solar radiation. According to the results of statistical analysis, urban cicadas with similar activity periods are influenced by species, especially with respect to urban dominant species, C. atrata. Forest cicadas were influenced by species, mainly M. opalifera, which is a forest dominant species. The results of the meteorological impact analysis were similar to those of the correlation analysis, and were influenced mainly by the temperature, and the influence of the insolation was more increased in the forests.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Korean Journal of Heritage: History & Science
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• v.45 no.4
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• pp.54-73
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• 2012

In this article, we examined the patterns of activities of the Sarmathians though in a humble measure, with a focus on the regions where the Sarmathian sheaths spreaded. One of the main weapons the mounted nomads like the Scythias, the Sarmathians, and the Alans used at war was a spear. Though complementary, a sword was the most convenient and appropriate weapon when fighting at a near distance, fallen from the horse to the ground. The Sarmathian swords continued the tradition of the Akinakes which the Scythias or the Persians used, but those of the Sarmathians showed some advances in terms of the easiness with which a sword was drawn out from a sheath, and the way the sheaths were worn to parts of a human body. It turns out that the Sarmathian sheaths, which were designed for the people to draw swords easily, having the sheaths attached to thighs through 4 bumps, spread extensively from Pazyryk, Altai, to South Siberia, Bactria, Parthia and Rome. The most noteworthy out of all the Sarmathian sheaths were the ones that were excavated from the 4th tomb in Tillatepe, Afghanistan which belonged to the region of Bactria. The owner of the fourth tomb of Tilla-tepe whose region was under the control of Kushan Dynasty at that time, was buried wearing Sarmathian swords, and regarded as a big shot in the region of Bactria which was also under the governance of Kushan Dynasty. The fact that the owner of the tomb wore two swords suggests that there had been active exchange between Bactria and Sarmathia. It seemed that the reason why the Sarmathians could play an important role in the exchange between the East and the West might have something to do with their role of supplying Chinese goods to Silk Road. That's why we are interested in how the copper mirrors of Han Dynasty, decoration beads like melon-type beads, crystal beads and goldring articulated beads, and the artifacts of South China which produced silks were excavated in the northern steppe route where the Sarmathians actively worked. Our study have established that the eye beads discovered in Sarmathian tomb estimated to have been built around the 1st century B.C. were reprocessed in China, and then imported to Sarmathia again. We should note the Huns as a medium between the Sarmathians and the South China which were far apart from each other. Thus gold-ring articulated beads which were spread out mainly across the South China has been discovered in the Huns' remains. On the other hand, between 2nd century B.C. and 2nd century A.D. which were main periods of the Sarmathians, it was considered that the traffic route connecting the steppe route and the South China might be West-South silk road which started from Yunnan, passed through Myanmar, Pakistan, and Afghanistan, and then went into the east of India. The West-south Silk road is presumed to have been used by nomadic tribes who wanted to get the goods from South China before the Oasis route was activated by the Han Dynasty's policy of managing the countries bordering on Western China.

• Korean Journal of Heritage: History & Science
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• v.49 no.3
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• pp.32-51
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• 2016

This study examines, based on the database of the Institute for the Translation of Korean Classics(ITKC), the garden plants and their symbolism, and the landscape culture recorded in 'Jipkyungjaeyoungsi(the Series of Poems on Gardens Poetry)' in relevance to traditional houses. First, Jipkyungjaeyoungsi had been continuously written since mid-Goryeo dynasty, when it was first brought in, until the late Joseon dynasty. It was mainly enjoyed by the upper class who chose the path of civil servants. 33 pieces of Jaeyoungsi(題詠詩) in 25 books out of a total of 165 books are related to residential gardens. The first person who wrote a poem in relation to this is believed to be Lee GyuBo(1168~1241) in the late Goryeo dynasty. He is believed to be the first person to contribute to the expansion of natural materials and the variation of entertainment in landscape culture with such books as 'Toesikjaepalyoung(退食齋八詠)', 'Gabeunjeungyukyoung(家盆中六詠)'and 'Gapoyukyoung(家圃六詠)'. Second, most of the poems used the names of the guesthouses. Out of the 33 sections, 19(57.5%) used 8 yeong(詠), then it was in the sequence of 4 yeong(詠), 6 yeong, 10 yeong, 14 yeong, 15 yeong, 16 yeong, 36 yeong(詠) and so on. In the poem writing, it appears to break the patterns of Sosangpalkyung(瀟湘八景) type of writings and is differentiated by (1) focusing on the independent title of the scenery, (2) combining the names of the place and landscape, (3) focusing on the name of the landscape. Third, the subtitles were derived from (1) mostly natural landscape focused on nature and garden plants(22 sections, 66.7%), (2) cultural landscape focused on landscape facilities such as guesthouses, ponds and pavilions(3 sections), (3) complex cultural scenery focused on the activities of people in nature(8 sections). Residents enjoy not only their aesthetic preferences and actual view, but the ideation of the scenery. Especially, they display attachment to and preference for vegetables and herbs, which had been neglected. Fourth, the percentage of deciduous tree population(17 species) rated higher(80.9%) compared to the evergreens(4 species). These aspects are similar results with the listed rate in 'Imwonkyungjaeji(林園經濟志)' by Seo YuGu [evergreen 18 species(21.2%) and deciduous trees 67 species(78.8%)] and precedent researches [Byun WooHyuk(1976), Jung DongOh(1977), Lee Sun(2006)]. Fifth, the frequency of the occurrence of garden plants were plum blossoms(14 times), bamboos(14 times), pine trees(11 times), lotus(11 times), chrysanthemum(10 times), willows(5 times), pomegranates(4 times), maple trees(14 times), royal foxglove trees, common crapemyrtle, chestnut trees, peony, plantains, reeds and a cockscombs(2 times). Thus, the frequency were higher with symbolic plants in relations to (1) Confucian norms(pine trees, oriental arbor vitae, plum blossoms, chrysanthemums, bamboos and lotus), (2) living philosophy of sustain-ability(chrysanthemum, willow), (3) the ideology of seclusion and seeking peace of mind(royal foxglove ree, bamboo). Sixth, it was possible to trace plants in the courtyard and outer garden, vegetable and herb garden. Many symbolic plants were introduced in the courtyard, and it became cultural landscape beyond aesthetic taste. In the vegetable and herb garden, vegetables, fruits and medicinal plants are apparently introduced for epigenetic use. The plants that were displayed to be observed and enjoyed were the sweet flag, pomegranate, daphne odora, chrysanthemum, bamboo, lotus and plum blossom. Seventh, it was possible to understand garden culture related to landscaping materials through poetic words such as pavilions, ponds, stream, flower pot, oddly shaped stones, backyard, orchard, herb garden, flower bed, chrysanthemum fence, boating, fishing, passing the glass around, feet bathing, flower blossom, forest of apricot trees, peach blossoms, stroking the pine tree, plum flower blossoming through the snow and frosted chrysanthemum.