• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.203-219
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• 2011

Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.35-45
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• 2008

In order to constructan ubiquitous computing environment, it is necessary to develop a technology that can recognize users and circumstances. In this regard, the question of recognizing and expressing user Context regardless of computer and language types has emerged as an important task under the heterogeneous distributed processing system. As a means to solve this task of representing user Context in the ubiquitous environment, this paper proposes to describe user Context as the most similar form of human thinking by using semantic web and fuzzy concept independentof language and computer types. Because the conventional method of representing Context using an usual collection has some limitations in expressing the environment of the real world, this paper has chosen to use Fuzzy OWL language, a fusion of fuzzy concept and standard web ontology language OWL. Accordingly, this paper suggests the following method. First we represent user contacted environmental information with a numerical value and states, and describe it with OWL. After that we transform the converted OWL Context into Fuzzy OWL. As a last step, we prove whether the automatic circumstances are possible in this procedure when we use fuzzy inference engine FiRE. With use the suggested method in this paper, we can describe Context which can be used in the ubiquitous computing environment. This method is more effective in expressing degree and status of the Context due to using fuzzy concept. Moreover, on the basis of the stated Context we can also infer the user contacted status of the environment. It is also possible to enable this system to function automatically in compliance with the inferred state.

• The Journal of Society for e-Business Studies
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• v.13 no.1
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• pp.1-20
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• 2008

Ontologies are expected in various areas as promising tools to improve communication among people and to achieve interoperability among systems. For communications between different business domains, building an ontology through integrating existing ontologies is more efficient way than building the ontology without them. However, integration of ontologies is very struggling work since languages, domains, and structures of ontologies are different from each other. In this paper, we suggest an Ontology Architecture which solves this problem by providing a systematic framework to classify ontologies from three kinds of viewpoints : language, domain range, constructs. The Ontology Architecture consists of 3 axes according to the 3 viewpoints : Ontology Meta Layer axis, Semantic Domain Layer axis, and Ontology Constructs Layer axis. Because 3 axes in Ontology Architecture are designed to improve the syntactic and semantic interoperability among ontologies, the integration of ontologies can be readily achieved.

• Convergence Security Journal
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• v.14 no.3_1
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• pp.21-26
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• 2014

Sports paradigm shift gave an opportunity to transform into production-oriented industry activities creating high value-added. Sports industrial cluster, organic union of qualitative industries producing and distributing of various sports derivative products, is necessary system in these sports paradigm shift. This study is intended to suggest the definition, construction, and direction for future of sports industrial cluster. In order to ensure the sustainable growth of sports industrial cluster, a growth strategy such as switching to the sementic cluster is needed. And the direction for future of sports industrial cluster were as following; finding the growth engine sports industry field according to the convergence, systematically promoting by setting the medium and long term development goals, constructing a wide industrial region for global competitiveness of sports industry.

• Journal of the Korean Society for information Management
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• v.27 no.3
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• pp.35-51
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• 2010

Emerging Web 2.0 services such as Twitter, Blogs, and Wikis alongside the poorlystructured and immeasurable growth of information requires an enhanced information organization approach. Ontology has received much attention over the last 10 years as an emerging approach for enhancing information organization. However, there is little penetration into current systems. The purpose of this study is to propose ontology implementation and methodology. To achieve the goal of this study, limitations of traditional information organization approaches are addressed and emerging information organization approaches are presented. Two ontology data models, RDF/OW and Topic Maps, are compared and then ontology development processes and methodology with topic maps based medical information retrieval system are addressed. The comparison of two data models allows users to choose the right model for ontology development.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.386-390
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• 2007

In this paper we present an Ontology Instance Management Tool. OntoManager is a user-friendly interactive ontology Instance management tool with webpage annotation tool and an image annotation tool. It supports the user with the task of creating and maintaining ontology-based OWL-markup, creating of OWL-instances, attributes and relationships. It include an ontology browser for the exploration of the ontology and instances and a HTML browser that will display the annotated parts of the text. And OntoManager is an image annotation tool that allows users to markup regions of an image with respect to concepts in an ontology. It provides the functionality to import images, ontologies, instance bases, perform markup, and export the resulting annotations to disk or the Web.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Convergence Security Journal
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• v.13 no.3
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• pp.63-69
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• 2013

This research analyze the current status pertaining to the sport industry's information system and information network in Korea to propose integrated information network of sport industry construction measures with the goal of optimizing vertical and horizontal network formation, and knowledge information sharing and dissemination. To construct integrated information network of sport industry, literature examination and meeting of experts to search for measures are utilized. This paper analyze the realities of the sport industry's information system and information network in Korea to propose the following when it comes to the measures to realize sport industry's information integration. First, DB for sport industry relate information that factored in the Ontology is being developed. In other words, it is necessary to design a DB that factors in the Semantic Web. Second, once the DB relate to the sport industry that factored in the Ontology is developed, it is necessary to build RSS/Atom based active network that enables exchange of organic information among them.

• Information Systems Review
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• v.12 no.2
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• pp.89-104
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• 2010

Recently, ubiquitous sensor network (USN) has been applied to many areas including environment monitoring. A few studies applied the USN to disaster prevention and emergency management, in particular, aiming to conserve cultural heritage. USN is an useful technology to do online real-time monitoring for the purpose of early detection of the fire which is a critical cause of damage and destruction of cultural heritages. It is necessary to online monitor the cultural heritages that human has a difficulty to access or their external appearance and beauty are important, by using the USN. However, there exists false warning from USN-based monitoring systems without human intervention. In this paper, we presented an alternative to resolve the problem by applying ontology. Our intelligent fire early detection systems for conserving cultural heritages are based on ontology and inference rules, and tested under laboratory environments.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.730-739
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• 2010

One of core technology in Web 2.0 is tagging, which is applied to multimedia data such as web document of blog, image and video etc widely. But unlike expectation that the tags will be reused in information retrieval and then maximize the retrieval efficiency, unacceptable retrieval results appear owing to toot limitation of tag. In this paper, in the base of preceding research about image retrieval through tag clustering, we design and implement a topic map generation system which is a semantic knowledge system. Finally, tag information in cluster were generated automatically with topics of topic map. The generated topics of topic map are endowed with mean relationship by use of WordNet. Also the topics are endowed with occurrence information suitable for topic pair, and then a topic map with semantic knowledge system can be generated. As the result, the topic map preposed in this paper can be used in not only user's information retrieval demand with semantic navigation but alse convenient and abundant information service.