• 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 Korea Information Processing Society Conference
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• 2014.11a
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• pp.1068-1072
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• 2014

We propose a collaborative social tagging for eBook using external DSL approach. The goal of this paper is (1) to provide DSL by which authors can write HTML5 rich contents ebook and tag resources, (2) to make users enhance book by tagging resources easily, (3) to make readers read rich book easily regardless of their devices types, (4) to provide ebook resources of RESTful address style by which other system can identify self-descriptive resources of book. To achieve the goal, we provide Bukle DSL language by which author and users can author and enhance ebook with ease. As a domainspecific language Bukle provides a simple yet expressive language for authoring and tagging books that would otherwise be more difficult to express with a general purpose language. Further work includes visual DSL approach and tools by using that the unskilled users could tag book easily. In order that future work also includes text-to-visual DSL transform engine. UX research is also required to tag and to author book. To tackle the above questions we are looking at using visual notation focusing visual syntax.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Journal of KIISE
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• v.43 no.1
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• pp.47-53
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• 2016

While the Internet develops rapidly, a huge amount of image data collected from smart phones, digital cameras and black boxes are being shared through social media sites. Generally, social images are handled by tagging them with information. Due to the ease of sharing multimedia and the explosive increase in the amount of tag information, it may be considered too much hassle by some users to put the tags on images. Image retrieval is likely to be less accurate when tags are absent or mislabeled. In this paper, we suggest a method of extracting tags from social images by using image content. In this method, CNN(Convolutional Neural Network) is trained using ImageNet images with labels in the training set, and it extracts labels from instagram images. We use the extracted labels for automatic image tagging. The experimental results show that the accuracy is higher than that of instagram retrievals.

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique 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 automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.181-195
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• 2010

The participation and share between personal users are the driving force of Web 2.0, and easily found in blog, social network, collective intelligence, social bookmarking and tagging. Among those applications, the social bookmarking lets Internet users to store bookmarks online and share them, and provides various services based on shared bookmarks which people think important.Delicious.com is the representative site of social bookmarking services, and provides a bookmark search service by using tags which users attach to the bookmarks. Our paper suggests a method re-ranking the ranks from Delicious.com based on user tags in order to provide personalized bookmark recommendations. Moreover, a method to consider bookmarks which have tags not directly related to the user query keywords is suggested by using tag network based on Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare the ranks by Delicious.com with new ranks of our system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.663-669
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• 2016

This study explores the effect of tagging in Social Network Services, especially Facebook, which has become popular as a marketing platform. In Facebook, users generally make recommendations using 'Like', 'Share', or 'Tag'. 'Tag' is different from 'Like' or 'Share' in that it can be used to deliver certain messages directly to specific people based on their interests or characteristics. Tagging can be categorized into rewarded tagging and non-rewarded tagging. As a result of our exploratory research, we found that non-rewarded tagging by certain users can indicate that the people, who are tagged, are interested in the contents of the users and share the same interest as them. Also, tagging indicates that these users want to share these services, such as restaurants and tours, with their friends who are tagged in the contents. Therefore, this study sheds light on the importance of the tagging function, as well as 'Like' and 'Share'.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.29-37
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• 2011

Recently, many user-participating web services have been used widely as the evolution of internet web technology has rapidly been developed. Users share various content and opinion on line using a site like ‘Social bookmarking.’ Users can share others’ bookmarking history and create tags while bookmarking web sites; we call it collaborative tagging. In this paper, we studied empirical analysis for widely used social bookmarking and collaborative tagging which the result shows minority of users is actively using the bookmarking and a few sites and tags are used by majority of the users. 24% users tagged 80%, 75% sites and 81% tags were tagged below than 3 times. Types of bookmarking activities were found different by users and early appointed tags get more frequency by majority. We also identified relative proportions of tags on certain sites are becoming convergence gradually. We expect the result of this paper will give opportunities to help further developing social bookmarking system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.43-51
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• 2012

Web 3.0 is the next-generation of the World Wide Web and is included two main platforms, semantic technologies and social computing environment. The basic idea of web 3.0 is to define structure data and link them in order to more effective discovery, automation, integration, and reuse across various applications. The semantic technologies represent open standards that can be applied on the top of the web. The social computing environment allows human-machine co-operations and organizing a large number of the social web communities. In the recent years, recommender systems have been combined with ontologies to further improve the recommendation by adding semantics to the context on the web 3.0. In this paper, we study previous researches about recommendation method and propose a recommendation method based on web 3.0. Our method scores documents based on context tags and social network services. Our social scoring model is computed by both a tagging score of a document and a tagging score of a document that was tagged by a user's friends.

• International Commerce and Information Review
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• v.13 no.4
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• pp.3-21
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• 2011

In this study, several experiments were designed to test the effectiveness of social media in export marketing. In particular, the experiments were made using Twitter and Facebook. The results showed that users' interest were able to increase the effects combined with B2B and B2C marketing events. The B2C marketing events could be made by personal target Event, Poll event, guest comments and social commerce. The B2B marketing was performed using Page Manager, Affiliate page, building and affiliate marketing group. Special features of Facebook such as social plug-in, Twitter integration, and Photo Tagging were found effective. A couple of implications were found in this study. First, the link between social media channel system was key success factor in effective export marketing. Second, the corporate marketing mix and social media consistent with the marketing mix strategy, communication between the managers and the managers' competencies were obtained for the key success factors.