• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.191-196
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• 2003

The Semantic web service is able to intelligently discover, execute, composite and monitor the Web Service. It constructs the ontology on Web Service and describes the Semantic Markup in the machine-readable form. The currently developing technologies of the Semantic Web Service discovery are DAML-S matchmaker in Carnegie Mellon University, Process Handbook in MIT and etc. In this paper, we propose the ontology language based on Topic Maps that supports the benefits and solves the problems of the Semantic Web Service discovery technologies .

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

• Journal of IKEEE
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• v.12 no.4
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• pp.217-224
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• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.

• The Journal of Society for e-Business Studies
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• v.12 no.1
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• pp.123-133
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• 2007

With the emergence of Semantic Web and Web 2.0, the paradigm shift of the Web is on resource-centered services. That is, the focus now moves from having just rich resources to the meta-information of describing the resources. The relevant standards, RDF(Resource Description Language) and Topic Maps, of describing the meta-information are defined and adopted by W3C and ISO respectively. Describing meta-information in such a XML form could be burdensome to participants. Semantic Wiki extended from 1)WikiWikiWeb is proposed to deal with the problem. It enables users to generate RDF meta-information about Wiki pages with simple usages of the grammar. We discuss the way of improving interoperability between Topic Maps-based semantic Wiki papges and RDF-based ones. The method proposed by RDFTM task force is present with the usage of high-level Wiki grammar for facilitating low-level transformation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.1-13
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• 2004

The Semantic Web introduces the next generation of the Web by establishing a semantic layer of machine-understandable data to enable machines (i.e intelligent agents) retrieve more relevant information and execute automated web services using semantic information. Ontology-related technologies are very important to evolve the World Wide Web of today into the Semantic Web in representation and share of semantic data. In this paper, we proposed and implemented the efficient ontology management system, K-Box, which constructs and manages ontologies using topic maps. We can use K-Box system to construct, store and retrieve ontologies. K-Box system has several components: Topicmap Factory, Topicmap Provider, Topicmap Query Processor, Topicmap Object Wrapper, Topicmap Cache Manager, Topicmap Storage Wrapper.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.85-102
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• 2004

Currently, developed technologies for semantic web services discovery are based on ontologies. These ontologies are DAML-S(DARPA Agent Markup Language for Services) and Process Handbook Project of MIT. These technologies have some problems for intelligent web services discovery. So, in this paper we analyzed those ontologies and proposed TM-S, Topic Maps for Services. TM-S is the presentation model for semantic web services. And TM-S includes benefits and complements weaknesses of those ontologies. And we proposed TMS-QL, TM-S Query Language. TMS-QL is query language for intelligent web services discovery. At last, we designed and implemented intelligent web service discovery system that deals TM-S ontology and TMS-QL

• Korean Journal of Oriental Medicine
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• v.11 no.2
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• pp.35-52
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• 2005

This study is to design a Semantic Navigation Retrieval System for Oriental Medicine Articles based on a XTM so that people can search and use them more effectively than before. Keywords extracted from articles are categorized 4 topics : herbs, prescription, disease, and action. Keywords analysis Ontology is modeled based on 4 topics and their relations, and then represented Topic maps. Next, Article analysis Ontology is consist of title, author, keywords, abstracts and organization Topics from metadata. Keywords and Article analysis Ontology were integrated through Keywords Topic. Korean Medical Article Retrieval System is optimistic in terms on search results supporting semantic navigation in the information service aspects and easier accessibility because all related information are semantically connected with each different DBs.

• Spatial Information Research
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• v.21 no.1
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• pp.1-13
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• 2013

This paper describes a GIS framework for geo-semantic information retrieval in mobile computing environments. We built geographic ontologies of POI (point of interest) and weather information for use in the combination of semantic, spatial, and temporal functions in a fully integrated database. We also implemented a geo-semantic app for Android-based smartphones that can extract more appropriate POIs in terms of user contexts and geographic ontologies and can visualize the POIs using Google Maps API (application programming interface). The feasibility tests showed our geo-semantic app can provide pertinent POI information according to mobile user contexts such as location, time, schedule, and weather. We can discover a baking CVS (convenience store) in the test of bakery search and can find out a drive-in theater for a not rainy day, which are good examples of the geo-semantic query using semantic, spatial, and temporal functions. As future work, we should need ontology-based inference systems and the LOD (linked open data) of various ontologies for more advanced sharing of geographic knowledge.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.15 no.1
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• pp.43-67
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• 2004

The purpose of this study is to design semantic web and policy direction for national knowledge and information management. The paper describes all the components needed to accomplish the objective: 1) creating unchangeable and unique identifiers for metadata elements, resources, and ontology classes and properties; 2) recommending active use of XML namespaces; 3) establishing metadata and application profile standards for national integrated searching; 4)developing a metadata registry to promote semantic interoperability among metadata; 5) discussing the need of creating ontologies using W3C OWL and ISO Topic Maps; 6) providing intelligent search services based on metadata; and 7) presenting future directions and tasks of national knowledge and information management.