• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.1
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• pp.26-42
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• 2022

The purpose of this study is to examine and understand the area and the original outer boundaries of Hamchunwon(含春苑), which was the outer royal garden of Gyeonghuigung Palace, which existed before the site of the Russian legation. The results of the study are as follows. First, examining the 3 types of drawings prepared for securing the Russian legation's site and constructing a new building, it was confirmed that two low peaks, which appear to be the original terrain of Hamchunwon, existed in the north and south directions inside the site. According to the initial plan of the of the legation's site, it appears that the entrance of the legation building is connected to the Saemunan-ro in the northwest. However, according to the report made at the time when the Russian temporary minister Veber purchased the legation's site, it was recorded that the site already had a narrow entrance and a dirt road in place, and hence, it was connected to Saemunan-ro. This fact makes it possible to learn that the line of movement for officials and the original gate were located to the northwest of the site planned as the entrance of the legation building towards Hamchunwon. Second, the site was created by cutting the top of the high hill at the time of the construction of the legation building, and as a result, a two tiered staircase typed terrace was built. The ground on which the main building and the secretary's building, etc., were erected was made by cutting the highest peak and solidifying it flat, and a large quantity of soil was used for grading. In the case of the northern area of the main building, the traces of leveling the terrain by cutting the mountains are apparent, and an observation typed garden with a walking path and pavilion was formed by utilizing the physical environment equipped with an easy view. This may be considered as a use which is consistent with the topographical conditions of creating an outer royal garden to block the civilian views on a high terrain overlooking the palace. Third, Hamchunwon's fences were partially exposed in the photos from the 1880s through the 1890s, which demonstrate the spatial changes made around the US, UK, and the Russian legations. As a result of the photo analysis performed, Hamchunwon occupies the northern area of the Russian legation's site, and it is estimated that the north, west, and east walls of the legation resembled those of Hamchunwon. The area to the south of the Russian legation was originally a place made available for civilian houses, and it was possible to examine the circumstances of purchasing dozens of civilian houses and farmlands according to various materials. Fourth, Hamchunwon, which was formed as the outer royal garden of Gyeongdeokgung Palace of Lord Gwanghaegun, lost its sense of place as an outer royal garden when the entire building of Gyeonghuigung Palace was torn down and used as a construction members during the reconstruction of Gyeongbokgung Palace, and faded away as the site was sold to Russia around 1885. The area where Hamchunwon used to be located transformed into a core space of the Russian legation where the main building and garden were located after the construction of the new building. Hence, Hamchunwon, which was limited to the northern area of the Russian legation, does not carry the temporal and spatial context with Gyeongungung Palace and Seonwonjeon which were constructed after 1897, and it is determined that the view of Seonwonjeon as Baehoorim or Baegyeongrim is not valid.