• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.387-395
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• 2000

기존의 BISAgent(Bookmark Information Sharing Agent)는 사용자에 의해 저장되는 북마크를 공유를 기반으로 협동적 웹 브라우징(Collaborative Web Browsing)을 통해 Information Overload를 해결코자 하였다. 하지만, 사용자의 북마크의 메타 정보 입력에 대한 부담은 Information Noise 발생의 원인이 되었고, 그로인한 부작용이 발생하였다. 본 연구에서는 BISAgent에 Ontology의 결합을 통해 사용자가 방문한 사이트의 내용 정보를 해석하여 개념화 하였으며, XML을 이용하여 표현하였다. 그럼으로써 북마크 메타정보의 자동화 및 북마크 자체의 자동 삽입을 통해 사용자의 부담을 최소화하는 효과를 가져왔다. 실험으로써 Ontology를 이용한 일반 HTML 문서의 개념화된 XML 문서로의 변환을 수행하였으며 북마크 정보 저장의 자동화를 위해 사이트 방문 패턴 해석을 실시하여 시스템의 성능을 평가하였다. 양적으로 많은 사용자 북마크를 추출하지는 못했지만 추출된 북마크는 사용자의 재방문이 이루어짐으로써 상당히 높은 정확성을 얻을 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.286-288
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• 2000

최근 웹에 대한 관심이 집중되면서 정보의 양이 지수적으로 증가하고 있다. 웹 사용자들은 정보 검색에 있어서 많은 어려움을 겪게 되었다. 이 문제를 해결하기 위해 정보검색(Information Retrieval) 시스템의 웹 환경으로의 적용이나 개인 적응형 에이전트(Personal Adaptive Agent)를 이용한 정보 여과(Information Filtering)에 대한 연구가 진행되어왔다. 본 논문에서는 BISAgent(Bookmark Information sharing Agent) 시스템이 사용자에게 효과적인 정보 검색을 제공함을 설명한다. BISAgent는 여러 사용자의 북마크 정보를 공유하여 협동적 정보 여과기법(Collaborative Filtering)을 이용한 협동적 웹 브라우징(Collaborative Web Browsing)을 수행한다. 이 시스템의 성능을 평가하기 위해 검색 결과의 개수를 통한 정보 여과의 양적 측면과 통계적 방법을 이용하여 정보 추천(information recommendation)의 정확성을 실험하였다.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1245-1255
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• 2010

In current Web 2.0 environment, one of the most core technology is social bookmarking which users put tags and bookmarks to their interesting Web pages. The main purpose of social bookmarking is an effective information service by use of retrieval, grouping and share based on user's bookmark information and tagging result of their interesting Web pages. But, current social bookmarking system uses the number of bookmarks and tag information separately in information retrieval, where the number of bookmarks stand for user's degree of interest on Web contents, information retrieval, and classification serve the purpose of tag information. Because of above reason, social bookmarking system does not utilize effectively the bookmark information and tagging result. This paper proposes a Web contents ranking algorithm combining bookmarks and tag information, based on preceding research on associative tag extraction by tag clustering. Moreover, we conduct a performance evaluation comparing with existing retrieval methodology for efficiency analysis of our proposed algorithm. As the result, social bookmarking system utilizing bookmark with tag, key point of our research, deduces a effective retrieval results compare with existing systems.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.455-462
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• 2001

The World Wide Web has become one of the major services provided through Internet. When searching the vast web space, users use bookmarking facilities to record the sites of interests encountered during the course of navigation. One of the typical problems arising from bookmarking is that the list of bookmarks lose coherent organization when the the becomes too lengthy, thus ceasing to function as a practical finding aid. In order to maintain the bookmark file in an efficient, organized manner, the user has to classify all the bookmarks newly added to the file, and update the folders. This paper introduces our learning agent called BClassifier that automatically classifies bookmarks by analyzing the contents of the corresponding web documents. The chief source for the training examples are the bookmarks already classified into several bookmark folders according to their subject by the user. Additionally, the web pages found under top categories of Yahoo site are collected and included in the training examples for diversifying the subject categories to be represented, and the training examples for these categories as well. Our agent employs naive Bayesian learning method that is a well-tested, probability-based categorizing technique. In this paper, the outcome of some experimentation is also outlined and evaluated. A comparison of naive Bayesian learning method alongside other learning methods such as k-Nearest Neighbor and TFIDF is also presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1911-1916
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• 2013

One significant challenge that arises in social tagging systems is the rapid increase in the number and diversity of the tags. As opposed to structured annotation systems, tags provide users an unstructured, open-ended mechanism to annotate and organize web-content. In this paper, we propose a scheme for URL recommendation that is based on a folksonomy which is comprised of user-defined tags, URL-keywords and the category folder name as the major element. This scheme will be further improved and implemented on a browser extension that recommends to users the best way to classify a particular URL.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.555-559
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• 2002

Internet users can input URL in Address window of browser or click address of site that is registered to Bookmark and sail the world of internet. But, need input of ID and password and mouse click action of 4-5 times to reach on target page that users want. This environment is very annoying work to users who approach specification page repeatedly every day. So, Homepage (first screen) of URL that want is not, Web Page Access Manager's development that can approach to target page that want by mouse click of short once. That may make web users can approach fast on target page, and minimise effort by web sailing.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.35-38
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• 2002

인터넷을 항해하는 동안, 사용자들은 관심있고 흥미있는 사이트들을 자신의 즐겨찾기 폴더에 저장하여 다음에 쉽게 접속을 할 수 있도록 한다. 왜냐하면, 수많은 사이트들을 기억할 수 없기 때문이다. 본 논문에서는 북마크 기능을 이용하여 단순히 즐겨찾기 폴더에 저장된 사이트들을 보여주고 관리하는 사용자관점이 아닌, 서버측면에서 사용자의 행태를 분석하는 방법을 제안하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.244-246
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• 2001

본 논문에서는 사용자가 관심 있는 문서를 카테고리별로 직접 분류해서 추가하던 작업을 자동으로 분류하고 추가할 수 있는 북 마크 에이전트를 제안한다. 북 마친 에이전트는 사용자가 브라우징 시 사용자 성향을 분석하여 관심 있는 문서를 얻을 수 있다. 문서 내에서 특징을 찾기 위해 TF.IBF를 사용하였으며 또한 단어의 가중치 부여와 유사도를 계산하기 위해 벡터 공간 모델을 사용하였다. 이 작업을 통해 부정적인 문서의 URL이 추가될 수 있으며 이러한 문제를 해결하기 위해서 사용자의 피드백을 이용하여 제거할 수 있도록 하였다.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.193-201
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• 2009

The use of tagging to describes web documents in the form of keyword has experienced rising popularity among various web services. Tagging also plays an important role in collaborative bookmarking services which can be regarded as an online favorite bookmark service. Tags which are created by users make it easier to search other users' bookmarks as well as user's own bookmarks. In this paper we analyze usage patterns of collaborative tagging for exploring factors influencing the number of tags in web documents and users. We discovered that user's characteristics have more effect on the tags than the web documents' characteristics. Moreover, leading users contribute to make a variety of tag than following users. Our study implies that more knowledge can be created through the incentives for leading user in order to improve the service quality of tagging service.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.39-49
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• 2009

In this paper, we classify web-based social network into two types: open and community, and model user behavior in social activities. After that, we also propose the combination of instant messaging and web system as the method of support asynchronous/synchronous social activities. Furthermore, we introduce ImCoWeb prototype system that supports both asynchronous social activities (ex. social bookmark, comment, rate, and data share) and synchronous ones (ex. real-time communication, file transfer, co-browsing, and co-work). Because it is built on the existing instant messaging, it reduces costs by reusing the facilities such as session management, user management, and security of instant messaging.