• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.75-94
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• 2010

This study designed a pilot system in which queries can be expanded through a tag ontology where equivalent, synonymous, or related tags are bound together, in order to improve the retrieval effectiveness of videos. We evaluated the proposed pilot system by comparing it to a tag-based system without tag control, in terms of recall and precision rates. Our study results showed that the mean recall rate in the structured folksonomy-based system was statistically higher than that in the tag-based system. On the other hand, the mean precision rate in the structured folksonomy-based system was not statistically higher than that in the tag-based system. The result of this study can be utilized as a guide on how to effectively use tags as social metadata of digital video libraries.

• Journal of Digital Contents Society
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• v.8 no.3
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• pp.255-262
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• 2007

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs, videos and other content. In this paper, we analyze the structure and basic knowledge of collaborative tagging systems as well as their dynamical aspects. We also present a retrieval system, TagPlus, using synonym tag that is derived from WordNet database. Specifically, TagPlus, a synonym tag based system has users retrieve images from Flickr system. The proposed system show the images tagged by not only the tag that users input but also the synonyms that are synonyms with the tag.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.205-207
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• 2001

웹 상의 HTML 문서들은 수시로 변경되고 있으며, 정보를 검색하는 웹사이트 또한 예외는 아니다. 다수의 웹 검색엔진들의 결과를 통합하는 메타 검색엔진은 각 검색엔진의 정보 변경에 민감해야 된다. 본 논문은, 수시로 변경되는 검색엔진들의 HTML 문서 정보를 메타 검색 엔진에 반영하기 위해, 자동적으로 검색엔진들의 질의 형태 변경과 검색 엔진의 검색 결과 HTML 문서의 구조 변경 탐지는 질의 결과가 반복되는 HTML 태그(tags) 문서 구조를 패턴(pattern)으로 이용한다. 패턴 발견 알고리즘은 문자열에서 규칙적으로 발생하는 패턴을 찾아내는 Jaak Vilo 알고리즘을 기반으로 HTML 문서를 처리할 수 있도록 확장하였다. 발견된 HTML 문서 패턴과 기존의 검색 엔진 HTML 페이지의 구조적 패턴 정보를 비교하여 문서 구조 변경을 탐지한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.152-154
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• 2009

본 논문에서는 다양하고 효율적인 활용을 위한 동영상 저장 방법을 제안한다. 메타데이터에 포함된 단어와 검색어의 일치성을 중요시하는 기존의 저장 및 검색과 달리, 동영상 자료의 메타데이터에서 구간을 나타내는 태그에 그 구간이 사용자의 요구에 부합되는 정도에 따라 유용성 수치를 부여하여 데이터베이스에 저장함으로써, 검색시에 유용성 수치를 이용하여 원하는 구간에 접근 할 수 있고 사용자의 목적에 일치하는 정도에 따라 범위 검색이 가능하고 히스토리 테이블을 추가, 다차원 큐브 생성을 가능케 하여 동영상 자료의 폭넓은 활용과 효율적인 검색이 가능하다.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.4
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• pp.291-297
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• 2013

Recently, the amount of web pages, which include various information, has been drastically increased according to the explosive increase of WWW usage. Therefore, the need for web page classification arose in order to make it easier to access web pages and to make it possible to search the web pages through the grouping. Web page classification means the classification of various web pages that are scattered on the web according to the similarity of documents or the keywords contained in the documents. Web page classification method can be applied to various areas such as web page searching, group searching and e-mail filtering. However, it is impossible to handle the tremendous amount of web pages on the web by using the manual classification. Also, the automatic web page classification has the accuracy problem in that it fails to distinguish the different web pages written in different forms without classification errors. In this paper, we propose the automatic web page classification system using meta-tag that can be obtained from the web pages in order to solve the inaccurate web page retrieval problem.

• Journal of KIISE
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• v.44 no.6
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• pp.581-586
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• 2017

For automatic web scraping, unnecessary components such as menus and advertisements need to be removed from web pages and main contents should be extracted automatically. A content block tends to be located in the middle of a web page. In particular, Korean web documents rarely include metadata and have a complex design; a suitable method of content extraction is therefore needed. Existing content extraction algorithms use the textual and structural features of content blocks because processing visual features requires heavy computation for rendering and image processing. In this paper, we propose a new content extraction method using the tag positions in HTML as a quasi-visual feature. In addition, we develop a tag rank position, a type of tag position not affected by text length, and show that gradient boosting with the tag rank position is a very accurate content extraction method. The result of this paper shows that the content extraction method can be used to collect high-quality text data automatically from various web pages.

• Journal of Information Management
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• v.40 no.2
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• pp.71-94
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• 2009

Indexing images using traditional indexing methods like taxonomy is not always efficient because of its visual content. This study examined how to apply folksonomies to image retrieval. To do this, first, we developed a category model for image tags found in Flickr. The model includes five categories and seventeen subcategories. Second, in order to evaluate the usefulness of the model to represent the various image tags as well as to investigate the end-user tagging behavior, three researchers classified the sampled image tags(141 most popular tags, 105 tags on three individual tag clouds and 3,848 image tags assigned on 156 images) according to the model. Finally, based on the research results, we proposed three methods for efficient image retrieval: extending folksonomies by combining them with ontologies; improving image retrieval efficiency using visual content and folksonomies; and updating taxonomy using folksonomies.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.50-54
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• 2007

정보기술은 통신 및 멀티미디어 기술의 발전에 힘입어 빠르게 발전되고 있으며, 이에 따른 데이타베이스의 기술이 공간데이타, XML, 비디오, 음성과 같은 다양한 멀티미디어 데이터 분야에 적용되고 있다. 비디오 데이타는 순차적인 특성을 가지며, 시간과 공간정보가 결합된 3차원 데이타로서 처리시간이 높은 작업이기 때문에 검색이나 브라우징이 대단히 비효율적이다. 본 논문에서는 비주얼리듬을 이용하여 비디오 데이타에서 대표 프레임(Key Frame)을 추출한 다음 XML을 이용한 태그 및 키워드 정보를 대표 프레임에 삽입하여 검색이나 브라우징을 할 수 있는 동영상 내용편집 도구(Authoring Tool for Video Contents)를 제안한다. 비주얼리듬은 3차원의 시공간적인 정보를 2차원으로 매핑한 정보로 IDCT(inverse Discrete Cosine Transform)과정 없이 픽셀 정보를 얻을 수 있어 처리속도가 빠르며 컷, 와이프, 디졸브 등의 편집효과를 효과적으로 구분할 수 있다. 그리고 XML 데이타에는 태그 및 정보와 함께 대표 프레임의 정보까지 저장되므로 유사 화면 검색이나 내용 기반 검색을 제공할 수 있다.