• Proceedings of the Korean Library and Information Science Society Conference
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• 2001.06a
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• pp.13-28
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• 2001

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.405-413
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• 2009

Tagging system is the system that allows internet users to assign new meta-data which is called tag to article, photo, video and etc. for facilitating searching and browsing of web contents. Tag cloud, a visual interface is widely used for browsing tag space. Tag cloud selects the tags with the highest frequency and presents them alphabetically with font size reflecting their popularity. However the conventional tag selection method includes known weaknesses. So, we propose a novel tag selection method Freshness, which helps to find fresh web contents. Freshness is the mean value of Kullback-Leibler divergences between each consecutive change of tag co-occurrence probability distribution. We collected tag data from three web sites, Allblog, Eolin and Technorati and constructed the system, 'Fresh Tag Cloud' which collects tag data and creates our tag cloud. Comparing the experimental results between Fresh Tag Cloud and the conventional one with data from Allblog, our one shows 87.5% less overlapping average, which means Fresh Tag Cloud outperforms the conventional tag cloud.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.41-42
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• 2011

DataCite 메타데이터 요소를 분석하여 OpenURL 학술 서비스 유형을 기술하기 위한 메타태그를 Key/Encoded-Value (KEV) 형식으로 확장 제안하였다. 학술 서비스 유형 분석을 위해 Scopus와 Web of Science, NDSL 서비스를 비교 검토하여 8개의 학술서비스 유형을 도출하였다. 또한 과학데이터 기술을 위한 DataCite 컨소시엄의 메타데이터 요소를 집중적으로 분석하여 9개의 대표속성을 도출 하였다.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.1
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• pp.117-135
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• 2010

We designed a structured folksonomy system in which queries can be expanded through tag control; equivalent, synonym or related tags are bound together, in order to improve the retrieval efficiency (recall and precision) of image data. Then, we evaluated the proposed system by comparing it to a tag-based system without tag control in terms of recall, precision, and user satisfaction. Furthermore, we also investigated which query expansion method is the most efficient in terms of retrieval performance. The experimental results showed that the recall, precision, and user satisfaction rates of the proposed system are statistically higher than the rates of the tag-based system, respectively. On the other hand, there are significant differences among the precision rates of query expansion methods but there are no significant differences among their recall rates. The proposed system can be utilized as a guide on how to effectively index and retrieve the digital content of digital library systems in the Library 2.0 era.

• Journal of Korean Library and Information Science Society
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• v.33 no.2
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• pp.235-258
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• 2002

Since 1991 when the first Web pages wore placed on the internet, information access for numerous Web sites has developed new indexing methods which are different from traditional methods. This paper, as a basic research, deals with Web indexing(Website indexing). Embedded indexing providing basics of Web indexing is examined, and essential META tags used in Web indexing are reviewed in brief. Finally, all the important issues of Web indexing are investigated in detail.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1411-1414
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• 2001

사용자의 주변환경은 일정한 형태의 정보가 아닌 이질적인 형태의 정보들이 혼재되어 있다. 이동컴퓨팅에서 사용자는 다양한 경로를 통하여 여러 가지 다른 형태의 정보들을 획득하고, 다양한 방법으로 이를 처리하게 된다. 실생활의 정보는 디지털환경에서 처리하기 위하여 다양한 방법을 적용하여 디지털정보로 표현되어야 한다. 디지털정보는 표현방법에 따라서 다양한 포맷형식을 갖게되며, 적절한 응용프로그램과 연결되어 실행된다. 본 논문에서는 이동컴퓨팅환경에서 태그를 기반으로 하여 사용자에게 일관되고, 추상화된 정보획득방법을 제시한다. 또한, 태그를 실제 데이터를 표현하는 메타데이터에 대응시키며, 데이터를 처리할 수 있는 적절한 프로그램과 연결하는 과정을 내부적으로 처리하는 태그기반 이동정보서비스를 구현하였다. 제안된 방법론은 이동사용자에게 입 출력의 간편성을 제공하고, 주변환경에 대한 적응성을 향상시킬 것이다.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.133-141
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• 2008

Social Web is turning current Web into social platform for knowing people and sharing information. This paper takes major social tagging systems as examples, namely delicious, flickr and youtube, to analyze the social phenomena in the Social Web in order to identify the way of mediating and linking social data. A simple Tag Ontology (TO) is proposed to integrate different social tagging data and mediate and link with other related social metadata. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tag ontology is also suggested as an applying field.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.94-96
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• 2001

웹 기반의 분산 환경에서 데이터를 공유, 사용하려는 노력은 끊임 없이 계속되어 왔다. 기존의 HTML 문서를 이용할 경우에는 그 언어자체가 가지고 있는 한계성 때문에 효과적으로 문서를 공유하기가 어렵다. 이에 대한 대안으로 XML을 이용한 문서 교환 방법이 제시되고 있다. 하지만 서로 다른 DTD를 기반으로 작성된 XML문서를 교환할 경우에는 문제가 발생하게 된다. DTD가 서로 다른 사용자에 의해서 작성되었기 때문에 XML 문서 내의 태그 뿐만 아니라 문서가 가지고 있는 그 구조 또한 서로 상이하게 된다. 본 논문에서는 상이한 DTD를 기반으로 작성된 XML문서를 교환할 경우에 고려 해야 하는 XML 문서의 구조적 상이성의 예를 보여주고 이에 대한 해결 알고리즘을 제시한다. 문서 구조의 상이성은 적절한 매핑 테이블과 트리 구조를 이용한 태그 변환 방법을 이용하여 해결할 수 있다. 데이터 레지스트리와 본 논문에서 제안한 문서의 구조와 태그 변환 방법을 사용하면 XML 문서를 효과적으로 교환 할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.