• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.425-428
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• 2010

멀티미디어 기기의 확산과 인터넷의 발달로 Flickr, Facebook 과 같은 사회적 네트워크를 기반으로 이미지 공유가 활발해졌다. 사회적 네트워크 사이트에서 이미지의 효율적인 검색과 관리를 위해서 태그를 이용하는 방법이 많이 사용되고 있다. 하지만 많은 양의 이미지에 수동으로 태그를 등록하는 것은 사용자에게 많은 시간과 노력을 요구한다. 태그 추천 기술은 자동으로 사용자에게 태그를 추천함으로써, 수동 태깅의 한계를 극복할 수 있는 방법이다. 본 논문에서는 사회적 네트워크를 기반으로 하는 폭소노미에서 사용자 사이의 사회적 관계를 사용자 들의 얼굴 정보를 이용하여 측정하고, 이를 활용하여 이미지 태그를 추천하는 기술을 제안한다. 제안하는 방법은 이미지의 시각 정보와 태그 분포뿐만 아니라 사용자 사이의 사회적 관계 정보를 추가로 활용한다. 실험을 통해서 제안하는 방법이 기존의 이미지 태그 추천 방법에 비해서 7% 향상된 태그 추천의 정확성을 보장하는 것을 증명하였다.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.24-27
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• 2008

As shown in Wikipedia, tagging or cross-linking through major key-words improves the readability of documents. Recently, the Semantic Web rises the importance of social tagging as a key feature of the Web 2.0 and Tag Cloud has emerged as its crucial phenotype. In this paper we provides an efficient method of automated keyword tagging based on controlled term collection, where the computational complexity of O(mN) - if pattern matching algorithm is used - can be reduced to O(mlogN) - if Information Retrieval is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that IR-based tagging speeds up 5.6 times compared with fast pattern matching algorithm.

• Proceedings of the Korean Society for Information Management Conference
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• 2006.08a
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• pp.63-70
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• 2006

이용자중심의 정보기술이 발전하면서 웹 2.0이라는 개념이 등장하고 이용자가 정보를 조직하는 사회적 태깅 방식의 웹사이트가 확산되고 있다. Connotea와 CiteULike는 웹 2.0이라는 개념의 일부인 사회적 태깅 방식을 학술영역에 본격적으로 도입한 최초의 서비스로서, 과거의 통제어휘 위주의 디렉토리 분류체계를 벗어나 이용자들이 참여하여 제공한 정보와 협력하여 부여한 키워드를 중심으로 자유롭게 콘텐트를 분류하고 관리하는 정보공유 포털서비스이다. 이 글에서는 이와 같은 이용자 참여를 유도하는 정보서비스동향을 개관하고 Connotea와 CiteULike의 두 서비스를 비교해보았다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.1
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• pp.137-150
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• 2009

This study has introduced folksonomy to general patterns of folksonomy tags for the university libraries that have practically implemented Library 2.0. From the results, we can see that average about 1.35 tag is used for one content. Typical pattern of the tags follow a power function that frequency of use decreases as No. of uses increases, 79.51% of tags are expressing topic of contents, and 84.61% of tags are tag of social motivation. The results of analysis on increase/decrease rate for tags divided into 4 quarters said that A university library has big differences from quarters while B university library has similar data between quarters. The users have used average 5.25 tags. Trends of the users can be divided into 3 groups according to tagging patterns of the users.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.1
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• pp.189-210
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• 2011

This study aims to explore how users are tagging in order to utilize a folksonomy and whether they understand the social and interactive aspects of tagging in three different folksonomic systems, Connotea (www.connotea.org), Delicious(http://delicious.com), and CiteULike(www.citeulike.org). The study uses internet questionnaires, qualitative diary studies, and follow-up interviews to understand twelve participants' tagging activities associated with folksonomic interactions. The flow charts developed from the twelve participants showed that tagging was a quite complex process, in which each tagging activity was interconnected, and a variety of folksonomic system features were employed. Three main tagging activities involved in the tagging processes have been identified: item selection, tag assignment, and tag searching and discovery. During the tag assignment, participants would describe their tagging motivations related to various types of tags. Their perception of the usefulness of types of tags was different when their purpose was for social sharing rather than personal information management. While tagging, participants recognized the social potential of a folksonomic system and used interactive aspects of tagging via various features of the folksonomic system. It is hoped that this empirical study will provide insight into theoretical and practical issues regarding users' perceptions and use of folksonomy in accessing, sharing, and navigating internet resources.

• Journal of Korean Library and Information Science Society
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• v.40 no.4
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• pp.381-400
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• 2009

This study intends to make a comprehensive inquiry into the meaning and limitations of Folksonomy, and to explore how to make full use of Folksonomy in library cataloging. To this end, this study examined as follows : (1) how the philosophical meaning of Folksonomy is different from traditional principles of library cataloging, (2) what the viewpoint of LIS scholars toward Folksonomy are, and how North American libraries have customized Folksonomy for their catalogs. In addition, (3) usefulness of Folksonomy in library catalogs is thoroughly discussed. Based on these, (4) the final discussion includes strategies for Korean LIS scholars and library practitioners to consider when applying Folksonomy to Korea library contexts.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.3
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• pp.151-163
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• 2010

It is necessary, due to changes in the information environment, to investigate problems in existing information retrieval systems. Ontologies and social tagging, which are a relatively new means of information organization, enable exploratory discovery of information. These two connect a thought of a user with the thoughts of numerous other people on the Internet. With these connection chains through the interactions, users are foraging information actively and exploratively. Thus, the purpose of this study is, through qualitative research methods, to identify numerous discovery facilitators provided by ontologies and social tagging, and to create an exploratory discovery model based on them. The results show that there are three uppermost categories in which 5, 4 and 4 subcategories are enumerated respectively. The first category, 'Browsing and Monitoring,' has 5 sub categories: Noticing the Needs, Being Aware, Perceiving, Stopping, and Examining a Resource. The second category, Actively Participating, has 4 categories: Constructing Meaning, Social Bookmarking and Tagging, Sharing on Social Networking, Specifying the Original Needs. The third category, Actively Extends Thinking, also has 4 categories: Social Learning, Emerging Fortuitous Discovery, Creative Thinking, Enhancing Problem Solving Abilities. This model could contribute to the design of information systems, which enhance the ability of exploratory discovery.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.18 no.2
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• pp.141-159
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• 2007

The purpose of this study was to try to conceptualize the Folksonomy, so called collaborative taggng or bookmarking and suggest its application on the Web information services. This paper explores how folksonomies could be used in web information services to enable end users to manage personal information spaces, get helped existing controlled vocabularies, and create and share their interests in online communities. Traditional classification system and philosophical issues on Folksonomy were reviewed in this paper in the context of internet based information and its services. The benefits and shortcomings of folksonomies are discussed. Some of the customizable features in existing library catalogue systems are reviewed to suggest other applicable features for web information services.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.826-833
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• 2009

Currently, the rise of social tagging has changing taxonomy to folksonomy. Tag represents a new approach to organizing information. Nonhierarchical classification allows data to be freely gathered, allows easy access, and has the ability to move directly to other content topics. Tag is expected to play a key role in clustering various types of contents, it is expand to network in the common interests among users. First, this paper determine the relationships among user, tags and resources in social tagging system and examine the circumstances of what aspects to users when creating a tag related to features of websites. Therefore, this study uses tags from the social bookmarking service 'del.icio.us' to analyze the features of tag words when adding a new web page to a list. To do this, websites features classified into 7 items, it is known as tag classification related to resources. Experiments were conducted to test the proposed classify method in the area of music, photography and games. This paper attempts to investigate the perspective in which users apply a tag to a webpage and establish the capacity of expanding a social service that offers the opportunity to create a new business model.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.