• International Journal of Contents
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• v.2 no.3
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• pp.1-5
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• 2006

Intelligent Content is defined as detailed information or fragment of content which contains a semantic data structure. This semantic structure makes possible to do various intelligent operations. There are wide range of content-oriented applications such as classification, retrieval, extraction, translation, presentation and question-answering. The concept of Intelligent Content is applied to various fields like MPEG and Semantic Web. In this paper, we discuss the several important researches of Intelligent Content and how to apply this conception to these fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3790-3803
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• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.523-524
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• 2009

Peer-to-Peer(P2P) 시스템은 분산된 대용량 데이터를 효율적으로 공유하게 하여 사용자들에게 제공되는 killer application 으로 최근까지 여러 분야에서 연구가 되고 있다. 하지만 P2P 네트워크에서 피어가 소유한 데이터나 공통 관심사 또는 사회적인 관계를 고려하지 않고 무작위로 오버레이가 구성되기 때문에 검색 결과의 제약이 발생한다. 따라서 본 논문에서는 P2P 오버레이상의 효율적인 데이터 검색을 위해서 각 피어가 가지고 있는 데이터와 공통의 관심사를 기반으로 유사성을 측정하여 semantic overlay를 구성하는 기법을 제안한다. 그리고 피어들 간의 semantic proximity는 데이터 요약 기법을 사용하여 측정되며 측정 과정상에서 popular content을 고려하여 semantic proximity의 왜곡현상을 방지하여 semantic link quality의 향상을 가져오는 방안을 도입한다.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.1
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• pp.193-211
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• 2008

The purpose of this study is to examine semantic interoperability in terms of metadata elements and content designation in context of digital libraries. This study analyzed 78 digital libraries implemented using Greenstone application with respect to metadata elements and their content designation. Using crosswalks of digital libraries' metadata elements, and comparisons of content designation in elements. this study identifies three aspects which can impact semantic interoperability. First, there were less than 25% core metadata elements even within homogeneous information communities. Second, discrepancy exists between element names and their usage. Third, different levels were identified when assigning content to designated elements in terms of integrity and completeness.

• International Journal of Knowledge Content Development & Technology
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• v.4 no.1
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• pp.39-51
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• 2014

The paper proposes that the Basic Concepts Classification (BCC) could serve as the controlled vocabulary for the Semantic Web. The BCC uses a synthetic approach among classes of things, relators, and properties. These are precisely the sort of concepts required by RDF triples. The BCC also addresses some of the syntactic needs of the Semantic Web. Others could be added to the BCC in a bottom-up process that carefully evaluates the costs, benefits, and best format for each rule considered.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.1
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• pp.227-243
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• 2017

This study proposed a structural and semantic framework for the characterization of events and segments in Web videos that permits content-based searches and dynamic video summarization. Although MPEG-7 supports multimedia structural and semantic descriptions, it is not currently suitable for describing multimedia content on the Web. Thus, the proposed metadata framework that was designed considering Web environments provides a thorough yet simple way to describe Web video contents. Precisely, the metadata framework was constructed on the basis of Chatman's narrative theory, three multimedia metadata formats (PBCore, MPEG-7, and TV-Anytime), and social metadata. It consists of event information, eventGroup information, segment information, and video (program) information. This study also discusses how to automatically extract metadata elements including structural and semantic metadata elements from Web videos.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1438-1444
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• 2018

Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest information. However, microblogs have word limits, and it has there is not enough information to analyze for content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only measures the similarity between the data represented as a vector space model, but also measures the semantic similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering algorithm.

• International Journal of Contents
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• v.2 no.1
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• pp.22-28
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• 2006

In this paper, we propose new semantic contents modeling using individual features, associative relations and visual features for efficiently supporting browsing and retrieval of video semantic contents. And we implement and design a browsing and retrieval system based on the semantic contents modeling. The browsing system supports annotation based information, keyframe based visual information, associative relations, and text based semantic information using a tree based browsing technique. The retrieval system supports text based retrieval, visual feature and associative relations according to the retrieval types of semantic contents.

• ETRI Journal
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• v.36 no.3
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• pp.429-438
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• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.199-206
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• 2009

The development of broadband multimedia content, a deaf sign language sign language is being used in education. Most of the content used in sign language training for Hangul word representation of sign language is sign language videos for the show. For the first time to learn sign language, sign language users are unfamiliar with the sign language characteristics difficult to understand, difficult to express the sign is displayed. In this paper, online, learning sign language to express the sign with reference to the attributes, Semantic Logic applying the sign language of multimedia content model for video-based platform is designed to study.