• Journal of KIISE:Databases
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• v.32 no.6
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• pp.593-602
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• 2005

Need for machine-understandable web (Semantic web) is increasing in order for users to exactly understand Web information resources and currently there are two main approaches to solve the problem. One is the Topic map developed by the ISO/IEC JTC 1 and the other is the RDF (Resource Description Framework), one of W3C standards. Semantic web supports all of the metadata of the Web information resources, thus the necessity of interoperability between the Topic map and the RDF is required. To address this issue, several conversion methods have been proposed. However, these methods have some problems such as loss of meanings, complicated structure, unnecessary nodes, etc. In this paper, a new method is proposed to resolve some parts of those problems. The method proposed is called NOWL (NO structural Warp and semantics Loss). NOWL method gives several contributions such as maintenance of the original a Topic map instance structure and elimination of the unnecessary nodes compared with the previous researches.

• 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.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.55-61
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• 2021

In this paper, we proposed a DeepLabv3+ based encoder-decoder model utilizing an attention mechanism for precise semantic segmentation. The DeepLabv3+ is a semantic segmentation method based on deep learning and is mainly used in applications such as autonomous vehicles, and infrared image analysis. In the conventional DeepLabv3+, there is little use of the encoder's intermediate feature map in the decoder part, resulting in loss in restoration process. Such restoration loss causes a problem of reducing segmentation accuracy. Therefore, the proposed method firstly minimized the restoration loss by additionally using one intermediate feature map. Furthermore, we fused hierarchically from small feature map in order to effectively utilize this. Finally, we applied an attention mechanism to the decoder to maximize the decoder's ability to converge intermediate feature maps. We evaluated the proposed method on the Cityscapes dataset, which is commonly used for street scene image segmentation research. Experiment results showed that our proposed method improved segmentation results compared to the conventional DeepLabv3+. The proposed method can be used in applications that require high accuracy.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2108-2115
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• 2000

The conventional Boolean retrieval systems based on vector spae model can provide the results of retrieval fast, they can't reflect exactly user's retrieval purpose including semantic information. Consequently, the results of retrieval process are very different from those users expected. This fact forces users to waste much time for finding expected documents among retrieved documents. In his paper, we designed a bayesian SOM(Self-Organizing feature Maps) in combination with bayesian statistical method and Kohonen network as a kind of unsupervised learning, then perform classifying documents depending on the semantic similarity to user query in real time. If it is difficult to observe statistical characteristics as there are less than 30 documents for clustering, the number of documents must be increased to at least 50. Also, to give high rank to the documents which is most similar to user query semantically among generalized classifications for generalized clusters, we find the similarity by means of Kohonen centroid of each document classification and adjust the secondary rank depending on the similarity.

• Journal of the Korean Society of Safety
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• v.23 no.5
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• pp.111-118
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• 2008

In usual safety signs are final means to transmit hazard information so that the importance of them cannot be emphasized too much. Nevertheless, in Korea, few people are interested in functions of safety signs so that evaluation of safety signs are seldom committed. This research was conducted to evaluate and compare perceptional characteristics of safety signs, especially "Fall" signs, by Semantic Differential Method and Multi-dimensional Scaling Method, with undergraduate students as well as industrial workers. According to research results on several signs evaluated high through suggested procedure, action inducibility was different for students majoring in different sciences, but it had common elements in the sense of 'openness' or 'arrangements'. Besides, perceptional images on safety signs were mainly recognized with bases of 'arrangement' for student group and 'simplicity' for industrial workers, respectively, and their maps corresponded well with each other by partial rotating so that students and workers seemed to recognize safety signs with similar factors though their name might be different. However, since perceptional characteristics including image map, comprehensibility, and action inducibility were similar for student group whereas those were not for worker group, it was concluded that the test for action inducibility would be absolutely necessary for safety signs for workers' group.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.23-26
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• 2006

최근 대부분의 이러닝(E-Learning)을 교육하는 사이트는 학습 콘텐츠를 검색하는 방법이 단순한 리스트의 나열과 택스트 매칭(Text matching)방법을 사용하는 단점이 있다. 이를 보완하기 위해 좀 더 컴퓨터가 정보 데이터의 의미를 분석하여 검색이 가능하도록 개념 네트워크인 시맨틱웹(Semantic Web)이 등장하였다. 본 논문에서는 이러한 시맨틱웹의 온톨로지(Ontology) 언어 중에 토픽맵(Topic Maps)을 사용하여 많은 양의 학습 정보 데이터를 쉽고도 정확하게 연결 지어 학습 콘텐츠에 대한 정보를 표현하고, 구조화할 수 있는 방법을 모색해 보고자 한다. 학습자의 관심분야 정보, 학습객체의 학습 권장자의 정보와 함께 학습 경험과 검색 빈도수를 분석한 협력 필터링과 학습 에이전트의 개인화 기법을 동시에 사용하여 선호도를 분석한다. 이 선호도를 가지고 학습자의 메타데이터를 생성하고, 로그 데이터를 따로 데이터베이스에 저장한다. 이러한 학습자의 정보와 학습 콘텐츠간의 정보를 상호 연결하여, 그 토픽맵을 사용하여 연관관계를 정의해 줌으로써 학업성취도를 높이고, 학습자 개개인의 성향에 가장 알맞은 학습 콘텐츠를 탐색해가는 네비게이터(Navigator)를 설계하였다.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.299-308
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• 2006

As recently XML is becoming the standard of exchanging web documents and public documentations, XML data are increasing in many areas. To retrieve the information about XML documents efficiently, the semantic web based on the ontology is appearing. The existing ontology has been constructed manually and it was time and cost consuming. Therefore in this paper, we propose the semi-automatic ontology generation technique using the data mining technique, the association rules. The proposed method solves what type and how many conceptual relationships and determines the ontology domain level for the automatic ontology generation, using the data mining algorithm. Appying the association rules to the XML documents, we intend to find out the conceptual relationships to construct the ontology, finding the frequent patterns of XML tags in the XML documents. Using the conceptual ontology domain level extracted from the data mining, we implemented the semantic web based on the ontology by XML Topic Maps (XTM) and the topic map engine, TM4J.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1681-1688
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• 2006

GIS systems have gradually been utilized in life information as well as special businesses such as traffic, sight-seeing, tracking, and disaster services. Most GIS services focus on showing stored information on maps, not providing a service to register and modify their preferred information. In this paper, we present a new method which reduces DXF map data into Simple Geographic Information File format using format conversion algorithms. We also present the prototype implementation of a GIS search system based on ontologies to support associated information. Our contribution is to propose a new digital map format to provide a fast map loading service and individual customized information on the map service.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.159-168
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• 2012

Relation in the new IT environment, such as the SNS, Cloud, Web3.0, has become an important factor. And these relations generate a transaction. However, existing relational database and graph database does not processe graph structure representing the relationships and transactions. This paper, we propose the technique that can be processed concurrently graph structures and transactions in a scalable complex network system. The proposed technique simultaneously save and navigate graph structures and transactions using the Topic Maps data model. Topic Maps is one of ontology language to implement the semantic web(Web 3.0). It has been used as the navigator of the information through the association of the information resources. In this paper, the architecture of the proposed technique was implemented and design using Cassandra - one of column type NoSQL. It is to ensure that can handle up to Big Data-level data using distributed processing. Finally, the experiments showed about the process of storage and query about typical RDBMS Oracle and the proposed technique to the same data source and the same questions. It can show that is expressed by the relationship without the 'join' enough alternative to the role of the RDBMS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.157-165
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• 2021

Recently, as the spatial resolution of satellite and aerial images has improved, various studies using remotely sensed data with high spatial resolution have been conducted. In particular, since the building extraction is essential for creating digital thematic maps, high accuracy of building extraction result is required. In this manuscript, building extraction models were generated using SegNet, U-Net, FC-DenseNet, and HRNetV2, which are representative semantic segmentation models in deep learning techniques, and then the evaluation of building extraction results was performed. Training dataset for building extraction were generated by using aerial orthophotos including various buildings, and evaluation was conducted in three areas. First, the model performance was evaluated through the region adjacent to the training dataset. In addition, the applicability of the model was evaluated through the region different from the training dataset. As a result, the f1-score of HRNetV2 represented the best values in terms of model performance and applicability. Through this study, the possibility of creating and modifying the building layer in the digital map was confirmed.