• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• The Korean Journal of Pesticide Science
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• v.5 no.3
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• pp.1-11
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• 2001

New technologies will have a large impact on the discovery of new herbicide site of action. Genomics, combinatorial chemistry, and bioinformatics help take advantage of serendipity through tile sequencing of huge numbers of genes or the synthesis of large numbers of chemical compounds. There are approximately $10^{30}\;to\;10^{50}$ possible molecules in molecular space of which only a fraction have been synthesized. Combining this potential with having access to 50,000 plant genes in the future elevates tile probability of discovering flew herbicidal site of actions. If 0.1, 1.0 or 10% of total genes in a typical plant are valid for herbicide target, a plant with 50,000 genes would provide about 50, 500, and 5,000 targets, respectively. However, only 11 herbicide targets have been identified and commercialized. The successful design of novel herbicides depends on careful consideration of a number of factors including target enzyme selections and validations, inhibitor designs, and the metabolic fates. Biochemical information can be used to identify enzymes which produce lethal phenotypes. The identification of a lethal target site is an important step to this approach. An examination of the characteristics of known targets provides of crucial insight as to the definition of a lethal target. Recently, antisense RNA suppression of an enzyme translation has been used to determine the genes required for toxicity and offers a strategy for identifying lethal target sites. After the identification of a lethal target, detailed knowledge such as the enzyme kinetics and the protein structure may be used to design potent inhibitors. Various types of inhibitors may be designed for a given enzyme. Strategies for the selection of new enzyme targets giving the desired physiological response upon partial inhibition include identification of chemical leads, lethal mutants and the use of antisense technology. Enzyme inhibitors having agrochemical utility can be categorized into six major groups: ground-state analogues, group specific reagents, affinity labels, suicide substrates, reaction intermediate analogues, and extraneous site inhibitors. In this review, examples of each category, and their advantages and disadvantages, will be discussed. The target identification and construction of a potent inhibitor, in itself, may not lead to develop an effective herbicide. The desired in vivo activity, uptake and translocation, and metabolism of the inhibitor should be studied in detail to assess the full potential of the target. Strategies for delivery of the compound to the target enzyme and avoidance of premature detoxification may include a proherbicidal approach, especially when inhibitors are highly charged or when selective detoxification or activation can be exploited. Utilization of differences in detoxification or activation between weeds and crops may lead to enhance selectivity. Without a full appreciation of each of these facets of herbicide design, the chances for success with the target or enzyme-driven approach are reduced.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• Journal of Plant Biotechnology
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• v.43 no.4
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• pp.450-456
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• 2016

Rhizoctonia leaf blight (large patch) has become a serious problem in Korean lawn grass, which is extremely hard to treat and develops mostly from the roots of lawn grass to wither it away. Rhizoctonia leaf blight (large patch) is caused by Rhizoctonia solani AG2-2 (IV). To develop zoysia japonica with strong disease tolerance against this pathogenic bacterium, ${\beta}-1,3-glucanase$ was cloned from zoysia japonica, which is one of the PR-Proteins known to play a critical role in plant defense reaction. ${\beta}-1,3-glucanase$ is known to be generated within the cells when plant tissues have a hypersensitive reaction due to virus or bacterium infection and secreted outside the cells to play mainly the function of resistance against pathogenic bacteria in the space between the cells. This study utilized the commonly preserved part in the sequence of corn, wheat, barley, and rice which had been researched for their disease tolerance among the ${\beta}-1,3-glucanase$ monocotyledonous plants. Based on the part, degenerate PCR was performed to find out the sequence and full-length cDNA was cloned. E.coli over-expression was conducted in this study to mass purify target protein and implement in vitro activation measurement and antibacterial test. In addition, to interpret the functions of ZjGlu1 gene, each gene-incorporating plant transformation vectors were produced to make lawn grass transformant. Based on ZjGlu1 protein, antibacterial activity test was conducted on 9 strains. As a result, R. cerealis, F. culmorum, R.solani AG-1 (1B), and T. atroviride were found to have antibacterial activity. The gene-specific expression amount in each organ showed no huge difference in the organs based upon the transformant and against 18s gene expression amount.

• The Journal of the Korean bone and joint tumor society
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• v.9 no.1
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• pp.124-129
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• 2003

Purpose: To analyze the clinical outcome and radiological features after surgical treatment of stage III giant cell tumor around the knee. Materials and Methods: 21 patients with stage III giant cell tumor around the knee joint, who were operated at our institutes between March 1991 and February 2000, were selected for this study. The average follow-up was 5.7 years (range, 1~9 years). After thorough curettage using high speed burr, cryosurgery and cementing with polymethymethacrylate (PMMA) were performed in 11 patients. 7 patients were treated with PMMA cementing (4 patients) or bone grafting (3 patients) after curettage without cryosurgery. Reconstruction with prosthesis composite allograft and knee fusion with Huckstep nail were performed in 3 patients with huge defect and joint perforation. Results: Local recurrence developed in 1 out of 11 patients who was treated with curettage and cementing with cryosurgery (9.1%) and 3 out of 7 patients who underwent curettage and cementing without cryosurgery (28.6%). Joint space narrowing more than 3mm was noted in 1 patient (9.1%), who treated with cryosurgery and anther patient (14.5%) who treated without cryosurgery. There was no local recurrence in case of wide resection and reconstruction. Conclusion: Thorough curettage and PMMA cementing with cryosurgery as an adjuvant is thought to be effective modalities in the treatment of stage 3 giant cell tumors around the knee. Wide resection and reconstruction can be reserved mainly for the cases of stage 3 giant cell tumor with significant cortical destruction and marked joint destruction, and the cases of local recurrence with poor bone stock.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.4
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• pp.1-11
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• 2020

Noksan is a green area in the form of a hill located inside Gyeongbokgung Palace, unrecognized as a cultural heritage space. This study analyzed the literature and the actual site to derive its landscape meaning by examining the background for the formation of Noksan and how it changed. As a result, the identity of Noksan was related to the geomagnetic vein, pine forest, and deers, and the following are its landscape meaning. First, several ancient maps, including the 「Map of Gyeongbokgung Palace」 depicted the mountain range continuing from Baegaksan(Bugaksan) Mountain to areas inside Gyeongbokgung Palace, and Noksan is a forest located on the geomantic vein, which continues to Gangnyeongjeon Hall and Munsojeon Hall. On Bukgwoldo(Map of Gyeongbokgung Palace), Noksan is depicted with Yugujeong Pavilion, Namyeogo Storage, office for the manager of Noksan, the brook on north and south, and the wall. It can be understood as a prototypical landscape composed of minimal facilities and the forest. Second, the northern palace walls of Gyeongbokgung Palace were constructed in King Sejong's reign. The area behind Yeonjo(king's resting place) up to Sinmumun Gate(north gate of the palace) was regarded as the rear garden when Gyeongbokgung Palace was constructed. However, a new rear garden was built outside the Sinmumun Gate when the palace was rebuilt. Only Noksan maintained the geomantic vein under the circumstance. However, the geographical features changed enormously during the Japanese colonial era when they constructed a huge official residence in the rear garden outside the Sinmumun Gate and the residence of the governor-general and road in the site of the Blue House. Moreover, Noksan was severed from the foothill of Baegaksan Mountain when 'Cheongwadae-ro(road)' was constructed between the Blue House and Noksan in 1967. Third, the significant characteristics and conditions of the forest, which became the origin of Noksan, were identified based on the fact that the geomatic state of the northeastern side of Gyeongbokgung Palace, the naecheongnyong area in geomantic terms(the innermost 'dragon vein' among the veins that stretched out from the central mountain toward the left side), and they planted pine trees to reinforce the 'ground vein' and the fact that it was expressed as the 'Pine Field' before the Japanese Invasion of Korea in 1592. The pine forest, mixed with oaks, cherries, elms, and chestnuts, identified through the excavation investigation, can be understood as the original vegetation landscape. Noksan's topography changed; a brook disappeared due to mounding, and foreign species such as acacia and ornamental juniper were planted. Currently, pine trees' ratio decreased while the forest is composed of oaks, mixed deciduous trees, some ailanthus, and willow. Fourth, the fact the name, 'Noksan,' came from the deer, which symbolized spirit, longevity, eternal life, and royal authority, was confirmed through an article of The Korea Daily News titled 'One of the seven deers in Nokwon(deer garden) in Gyeongbokgung Palace starved to death.'

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.1
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• pp.40-52
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• 2016

Buyongjeong, a pavilion in the Rear Garden of Changdeok Palace, was appointed as Treasure No. 1763 on March 2, 2012, by the South Korea government since it shows significant symmetry and proportion on its unique planar shape, spatial configuration, building decoration, and so forth. However, the designation of Treasure selection was mainly evaluated by concrete science, in that the selection has not clearly articulated how and why Buoungjeong was constructed as a present unique form. Therefore, this study aims to clarify the identity of Buyongjeong at the time of construction by considering its historical, ideological, philosophical background and building intention. Summary are as follows: First, Construction backgrounds and characters of Buyongjeong: Right after the enthronement, King Jeongjo had founded Kyujanggak(奎章閣), and sponsored civil ministers who were elected by the national examination, as a part of political reform. In addition, he established his own political system by respecting "Kaksin(閣臣)", Kyujanggak's officials as much as "Kain(家人)", internal family members. King Jeongjo's aggressive political reform finally enabled King's lieges to visit King's Rear Garden. In the reign of King Jeongjo's 16th year(1792), Naekaksangjohoe(內閣賞釣會) based on "Kaksin" was officially launched and the Rear Garden visitation became a regular meeting. The Rear Garden visitation consisted of "Sanghwajoeoyeon(賞花釣魚宴)" - enjoying flowers and fishing, and activities of "Nanjeongsugye". Afterward, it eventually became a huge national event since high rank government officials participated the event. King Jeongjo shared the cultural activities with government officials together to Buyongjeong as a place to fulfill his royal politics. Second, The geographical location and spatial characteristics of Buyongjeong: On the enthronement of King Jeongjo(1776), he renovated Taeksujae. Above all, aligning and linking Gaeyuwa - Taeksujae - a cicular island - Eosumun - Kyujangkak along with the construction axis is an evidence for King Jeongjo to determine how the current Kyujangkak zone was prepared and designed to fulfill King Jeonjo's political ideals. In 17th year(1793) of the reign of King Jeongjo, Taeksujae, originally a square shaped pavilion, was modified and expanded with ranks to provide a place to get along with the King and officials. The northern part of Buyongjeong, placed on pond, was designed for the King's place and constructed one rank higher than others. Discernment on windows and doors were made with "Ajasal" - a special pattern for the King. The western and eastern parts were for government officials. The center part was prepared for a place where government officials were granted an audience with the King, who was located in the nortern part of Buyongjeong. Government officials from the western and eastern parts of Buyongjeong, could enter the central part of the Buyongjeong from the southern part by detouring the corner of Buyongjeong. After all, Buyongjeong is a specially designed garden building, which was constructed to be a royal palace utilizing its minimal space. Third, Cultural Values of Buyongjeong: The Buyongjeong area exhibits a trait that it had been continuously developed and it had reflected complex King's private garden cultures from King Sejo, Injo, Hyunjong, Sukjong, Jeongjo and so forth. In particular, King Jeongjo had succeded physical, social and imaginary environments established by former kings and invited their government officials for his royal politics. As a central place for his royal politics, King Jeongjo completed Buyongjeong. Therefore, the value of Buyongjeong, as a garden building reflecting permanency of the Joseon Dynasty, can be highly evaluated. In addition, as it reflects Confucianism in the pavilion - represented by distinguishing hierarchical ranks, it is a unique example to exhibit its distinctiveness in a royal garden.

• Journal of Distribution Science
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• v.8 no.3
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• pp.49-56
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• 2010

At the initial stage of Internet advertising, banner advertising came into fashion. As the Internet developed into a central part of daily lives and the competition in the on-line advertising market was getting fierce, there was not enough space for banner advertising, which rushed to portal sites only. All these factors was responsible for an upsurge in advertising prices. Consequently, the high-cost and low-efficiency problems with banner advertising were raised, which led to an emergence of keyword advertising as a new type of Internet advertising to replace its predecessor. In the beginning of 2000s, when Internet advertising came to be activated, display advertisement including banner advertising dominated the Net. However, display advertising showed signs of gradual decline, and registered minus growth in the year 2009, whereas keyword advertising showed rapid growth and started to outdo display advertising as of the year 2005. Keyword advertising refers to the advertising technique that exposes relevant advertisements on the top of research sites when one searches for a keyword. Instead of exposing advertisements to unspecified individuals like banner advertising, keyword advertising, or targeted advertising technique, shows advertisements only when customers search for a desired keyword so that only highly prospective customers are given a chance to see them. In this context, it is also referred to as search advertising. It is regarded as more aggressive advertising with a high hit rate than previous advertising in that, instead of the seller discovering customers and running an advertisement for them like TV, radios or banner advertising, it exposes advertisements to visiting customers. Keyword advertising makes it possible for a company to seek publicity on line simply by making use of a single word and to achieve a maximum of efficiency at a minimum cost. The strong point of keyword advertising is that customers are allowed to directly contact the products in question through its more efficient advertising when compared to the advertisements of mass media such as TV and radio, etc. The weak point of keyword advertising is that a company should have its advertisement registered on each and every portal site and finds it hard to exercise substantial supervision over its advertisement, there being a possibility of its advertising expenses exceeding its profits. Keyword advertising severs as the most appropriate methods of advertising for the sales and publicity of small and medium enterprises which are in need of a maximum of advertising effect at a low advertising cost. At present, keyword advertising is divided into CPC advertising and CPM advertising. The former is known as the most efficient technique, which is also referred to as advertising based on the meter rate system; A company is supposed to pay for the number of clicks on a searched keyword which users have searched. This is representatively adopted by Overture, Google's Adwords, Naver's Clickchoice, and Daum's Clicks, etc. CPM advertising is dependent upon the flat rate payment system, making a company pay for its advertisement on the basis of the number of exposure, not on the basis of the number of clicks. This method fixes a price for advertisement on the basis of 1,000-time exposure, and is mainly adopted by Naver's Timechoice, Daum's Speciallink, and Nate's Speedup, etc, At present, the CPC method is most frequently adopted. The weak point of the CPC method is that advertising cost can rise through constant clicks from the same IP. If a company makes good use of strategies for maximizing the strong points of keyword advertising and complementing its weak points, it is highly likely to turn its visitors into prospective customers. Accordingly, an advertiser should make an analysis of customers' behavior and approach them in a variety of ways, trying hard to find out what they want. With this in mind, her or she has to put multiple keywords into use when running for ads. When he or she first runs an ad, he or she should first give priority to which keyword to select. The advertiser should consider how many individuals using a search engine will click the keyword in question and how much money he or she has to pay for the advertisement. As the popular keywords that the users of search engines are frequently using are expensive in terms of a unit cost per click, the advertisers without much money for advertising at the initial phrase should pay attention to detailed keywords suitable to their budget. Detailed keywords are also referred to as peripheral keywords or extension keywords, which can be called a combination of major keywords. Most keywords are in the form of texts. The biggest strong point of text-based advertising is that it looks like search results, causing little antipathy to it. But it fails to attract much attention because of the fact that most keyword advertising is in the form of texts. Image-embedded advertising is easy to notice due to images, but it is exposed on the lower part of a web page and regarded as an advertisement, which leads to a low click through rate. However, its strong point is that its prices are lower than those of text-based advertising. If a company owns a logo or a product that is easy enough for people to recognize, the company is well advised to make good use of image-embedded advertising so as to attract Internet users' attention. Advertisers should make an analysis of their logos and examine customers' responses based on the events of sites in question and the composition of products as a vehicle for monitoring their behavior in detail. Besides, keyword advertising allows them to analyze the advertising effects of exposed keywords through the analysis of logos. The logo analysis refers to a close analysis of the current situation of a site by making an analysis of information about visitors on the basis of the analysis of the number of visitors and page view, and that of cookie values. It is in the log files generated through each Web server that a user's IP, used pages, the time when he or she uses it, and cookie values are stored. The log files contain a huge amount of data. As it is almost impossible to make a direct analysis of these log files, one is supposed to make an analysis of them by using solutions for a log analysis. The generic information that can be extracted from tools for each logo analysis includes the number of viewing the total pages, the number of average page view per day, the number of basic page view, the number of page view per visit, the total number of hits, the number of average hits per day, the number of hits per visit, the number of visits, the number of average visits per day, the net number of visitors, average visitors per day, one-time visitors, visitors who have come more than twice, and average using hours, etc. These sites are deemed to be useful for utilizing data for the analysis of the situation and current status of rival companies as well as benchmarking. As keyword advertising exposes advertisements exclusively on search-result pages, competition among advertisers attempting to preoccupy popular keywords is very fierce. Some portal sites keep on giving priority to the existing advertisers, whereas others provide chances to purchase keywords in question to all the advertisers after the advertising contract is over. If an advertiser tries to rely on keywords sensitive to seasons and timeliness in case of sites providing priority to the established advertisers, he or she may as well make a purchase of a vacant place for advertising lest he or she should miss appropriate timing for advertising. However, Naver doesn't provide priority to the existing advertisers as far as all the keyword advertisements are concerned. In this case, one can preoccupy keywords if he or she enters into a contract after confirming the contract period for advertising. This study is designed to take a look at marketing for keyword advertising and to present effective strategies for keyword advertising marketing. At present, the Korean CPC advertising market is virtually monopolized by Overture. Its strong points are that Overture is based on the CPC charging model and that advertisements are registered on the top of the most representative portal sites in Korea. These advantages serve as the most appropriate medium for small and medium enterprises to use. However, the CPC method of Overture has its weak points, too. That is, the CPC method is not the only perfect advertising model among the search advertisements in the on-line market. So it is absolutely necessary that small and medium enterprises including independent shopping malls should complement the weaknesses of the CPC method and make good use of strategies for maximizing its strengths so as to increase their sales and to create a point of contact with customers.