• Journal of the Korean Society for Library and Information Science
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• v.48 no.4
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• pp.91-113
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• 2014

Although social metadata has strengths in creating amount of user-contributed resource descriptions, its function is limited because of its non-systematic characteristics. This research proposed an alternative approach to semantic organization of social metadata. It analyzed the semantics of tags created in LibraryThing in order to provide bibliographic categories for describing information resources. Social information Architecture is adopted in generating the bibliographic categories so that social metadata framework can be constructed. This framework can provide the conceptual foundations for semantically organizing social metadata and is expected to be applied to the existing approaches to automatically organize social metadata.

• Journal of the Korean Society for information Management
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• v.28 no.4
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• pp.161-182
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• 2011

Metadata and folksonomy are two main approaches in representing, organizing, and retrieving resources in the current information environment. Many researches have conducted studies to combine of metadata and folksonomy in order to utilize the strengths of both approaches. This research proposed an approach to utilize both metadata and folksonomy in representing resources by using microcontents. Microcontents in this research is a conceptual structure that reflects dynamic characteristics of folksonomy and the structure of metadata. By connecting folksonomy with metadata through this microcontents structure, both approaches can maximize their strengths and minimize their weaknesses in representing, organizing, and retrieving resources.

• 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 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.75-77
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• 2021

A variety of search engines are currently in use. The search engine supports the retrieval of data required by users through three stages: crawling, index generation, and output of search results based on meta-tag information. However, a large number of documents obtained by searching for keywords are often unrelated or scarce. Because of these problems, it takes time and effort to grasp the content from the search results and classify the accuracy. The index of search engines is updated periodically, but the criteria for weighted values and update periods are different from one search engine to another. Therefore, this paper uses the LSTM model, which extracts the relationship between keywords entered by the user and documents instead of the existing search engine, and improves the relationship between keywords and documents by entering keywords that the user wants to find.

• 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.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.749-757
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• 2009

A Internet user can easily access to the target information by web searching using some key-words or categories in the present Internet environment. When some meta-data which represent attributes of several data structures well are used, then more accurate result which is matched with the intention of users can be provided. This study proposes a multiple dimensional vector tagging method for the small web user group who interest in maintaining and sharing the bookmark for common interesting topics. The proposed method uses vector tag method for increasing the effect of categorization, management, and retrieval of target information. The vector tag composes with two or more components of the user defined priority. The basic vector space is created time of information and reference value. The calculated vector value shows the usability of information and became the metric of ranking. The ranking accuracy of the proposed method compares with that of a simply link structure, The proposed method shows better results for corresponding the intention of users.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.848-861
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• 2015

In this paper, we introduce a new web-based PR video making service system. Many video editing tools have required tough editing skill or scenario planning stage for a just simple PR video making. Some users may prefer a simple and fast way than sophisticated and complex functionality. To solve this problem, it is important to provide easy user interface and intelligent classification and recommendation scheme. Therefore, we propose a new template classification and recommendation scheme using a topic modeling method. The proposed scheme has the big advantage of being able to handle the unstructured meta data as well as structured one.

• Journal of KIISE
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• v.42 no.3
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• pp.413-418
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• 2015

In this paper, we design new video metadata schema for searching video segments to create UEC (User Edited Contents). The proposed video metadata schema employs hierarchically structured units of 'Title-Event-Place(Scene)-Shot', and defines the fields of the semantic information as structured form in each segment unit. Since this video metadata schema is defined by analyzing the structure of existing UECs and by experimenting the tagging and searching the video segment units for creating the UECs, it helps the users to search useful video segments for UEC easily than MPEG-7 MDS (Multimedia Description Scheme) which is a general purpose international standard for video metadata schema.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.557-561
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• 2021

A meatadata has become an essential element in order to recommend video content to users. However, it is passively generated by video content providers. In the paper, a method for automatically generating metadata was studied in the existing manual metadata input method. In addition to the method of extracting emotion tags in the previous study, a study was conducted on a method for automatically generating metadata for genre and country of production through movie audio. The genre was extracted from the audio spectrogram using the ResNet34 artificial neural network model, a transfer learning model, and the language of the speaker in the movie was detected through speech recognition. Through this, it was possible to confirm the possibility of automatically generating metadata through artificial intelligence.