• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.703-717
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• 2006

The Semantic Web has the objective of developing universal media in which machine-processable semantic information can be represented and shared, and it is therefore important to distribute ontologies that represent this kind of semantic information to the Web and make them available to multiple parties. However, the current ontology authoring tools are not operating on the Web, which makes it difficult to distribute ontologies directly to the Web and to create and edit them collaboratively with other people. This paper proposes a framework that facilitates the ontology construction and sharing, realizing easy distribution of ontologies to the Web. Wiki is one of the frameworks for collaborative construction and sharing of knowledge on the Web, and Wiki contents consist of natural language texts and simple markup language for visualization. For better collaboration in creating and sharing ontologies, this paper suggests the Semantic Wiki that embodies the Semantic Web features to the existing Wiki system. The Semantic Wiki framework facilitates the collaboration in ontology co-authoring and sharing for people, and at the same time, makes it possible for the agent software to easily manage the ontology information. Eventually, the Semantic Wiki system accomplishes various tasks including the semantic view, the semantic navigation, and the semantic query.

• The Journal of Korean Association of Computer Education
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• v.14 no.3
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• pp.37-49
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• 2011

A user can use resources on the Semantic Web using a Semantic Web browser. In order to utilize the functions of Semantic Web browsers in education, we compared the functions of well-known Semantic Web browsers such as Tabulator, Contextual Search Browser (CSB), Magpie, and Piggy Bank. In order to utilize Semantic Web browsers in education, a user needs to understand the features of each Semantic Web browser and our work can help both teachers and students. Tabulator is an RDF browser that can help to check whether resources can be used for learning and relevance of resources. CSB can be used to search educational resources using a conrtext file that contains the subjects of learning. It can also help learning by showing semantic web resources in the form of triple set as well as by supporting highlighting function. Magpie can help learners without basic knowledge on learning materials by providing interpretation based on a glossary file and related background knowledge. Piggy Bank supports conversion of web resources into semantic web resources and allows to browse semantic web resources in various views as well as to share semantic web resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.53-60
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• 2012

In this paper, we proposes a new document summarization method using the expanded query by wikipedia and the semantic feature representing inherent structure of document set. The proposed method can expand the query from user's initial query using the relevance feedback based on wikipedia in order to reflect the user require. It can well represent the inherent structure of documents using the semantic feature by the non-negative matrix factorization (NMF). In addition, it can reduce the semantic gap between the user require and the result of document summarization to extract the meaningful sentences using the expanded query and semantic features. The experimental results demonstrate that the proposed method achieves better performance than the other methods to summary document.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.87-97
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• 2023

Unmanned aerial vehicles (UAVs) can capture high-resolution imagery from a variety of viewing angles and altitudes; they are generally limited to collecting images of small scenes from larger regions. To improve the utility of UAV-appropriated datasetsfor use with deep learning applications, multiple datasets created from variousregions under different conditions are needed. To demonstrate a powerful new method for integrating heterogeneous UAV datasets, this paper applies a combined segmentation network (CSN) to share UAVid and semantic drone dataset encoding blocks to learn their general features, whereas its decoding blocks are trained separately on each dataset. Experimental results show that our CSN improves the accuracy of specific classes (e.g., cars), which currently comprise a low ratio in both datasets. From this result, it is expected that the range of UAV dataset utilization will increase.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.680-687
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• 2005

This study presents a schema matching technique which can be applied to XML semantic model of structural calculation reports of steel-box bridges. The semantic model of structural calculation documents was developed by extracting the optimized common elements from the analyses of various existing structural calculation documents, and the standardized semantic model was schematized by using XML Schema. In addition, the similarity measure technique and the relaxation labeling technique were employed to develop the schema matching algorithm. The former takes into account the element categories and their features, and the latter considers the structural constraints in the semantic model. The standardized XML semantic model of steel-box bridge's structural calculation documents called target schema was compared with existing nonstandardized structural calculation documents called primitive schema by the developed schema matching algorithm Some application examples show the importance of the development of standardized target schema for structural calculation documents and the effectiveness and efficiency of schema matching technique in the examination of the degree of document standardization in structural calculation reports.