• Journal of the HCI Society of Korea
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• v.10 no.1
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• pp.5-17
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• 2015

Nowadays various public data have been serviced to the public. Through the opening of public data, the transparency and effectiveness of public policy developed by governments are increased and users can lead to the growth of industry related to public data. Since end-users of using public data are citizens, it is very important for everyone to figure out the meaning of public data using proper visualization techniques. In this work, to indicate the significance of widespread public data, we consider UN voting record as public data in which many people may be interested. In general, it has high utilization value by diplomatic and educational purposes, and is available in public. If we use proper data mining and visualization algorithms, we can get an insight regarding the voting patterns of UN members. To visualize, it is necessary to measure the voting similarity values among UN members and then a social graph is created by the similarity values. Next, using a graph layout algorithm, the social graph is rendered on the screen. If we use the existing method for visualizing the social graph, it is hard to understand the meaning of the social graph because the graph is usually dense. To improve the weak point of the existing social graph visualization, we propose Friend-Matching, Friend-Rival Matching, and Bubble Heap algorithms in this paper. We also validate that our proposed algorithms can improve the quality of visualizing social graphs displayed by the existing method. Finally, our prototype system has been released in http://datalab.kunsan.ac.kr/politiz/un/. Please, see if it is useful in the aspect of public data utilization.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1365-1377
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• 2019

Summarizing the differences in Chinese-Vietnamese bilingual news plays an important supporting role in the comparative analysis of news views between China and Vietnam. Aiming at cross-language problems in the analysis of the differences between Chinese and Vietnamese bilingual news, we propose a new method of summarizing the differences based on an undirected graph model. The method extracts elements to represent the sentences, and builds a bridge between different languages based on Wikipedia's multilingual concept description page. Firstly, we calculate the similarity between Chinese and Vietnamese news sentences, and filter the bilingual sentences accordingly. Then we use the filtered sentences as nodes and the similarity grade as the weight of the edge to construct an undirected graph model. Finally, combining the random walk algorithm, the weight of the node is calculated according to the weight of the edge, and sentences with highest weight can be extracted as the difference summary. The experiment results show that our proposed approach achieved the highest score of 0.1837 on the annotated test set, which outperforms the state-of-the-art summarization models.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.

• Korean Journal of Microbiology
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• v.39 no.2
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• pp.67-75
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• 2003

One large topic in comparative genomics is to predict functional annotation by classifying protein sequences. Computational approaches for function prediction include protein structure prediction, sequence alignment and domain prediction or binding site prediction. This paper is on another computational approach searching for sets of homologous sequences from sequence similarity graph. Methods based on similarity graph do not need previous knowledges about sequences, but largely depend on the researcher's subjective threshold settings. In this paper, we propose a genome sequence clustering method of iterative testing and graph decomposition, and a simple method to calculate a strict threshold having biochemical meaning. Proposed method was applied to known bacterial genome sequences and the result was shown with the BAG algorithm's. Result clusters are lacking some completeness, but the confidence level is very high and the method does not need user-defined thresholds.

• Design Convergence Study
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• v.14 no.4
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• pp.133-150
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• 2015

In this study, we proposed Interactive Visualization methods that can be analyzed relations between nations in various viewpoints such as period, issue using total 5211 of the UN General Assembly voting data.For this research, we devised a similarity matrix between nations and developed two visualization method based similarity matrix. The first one is Network Graph Visualization that can be showed relations between nations which participated in the vote of the UN General Assembly like Social Network Graph by year. and the second one is Proximity based Circular Visualization that can be analyzed relations between nations focus on one nation or Changes in voting patterns between nations according to time. This study have a great signification. that's because we proposed Proximity based Circular Visualization methods which merged Line and Circle Graph for network analysis that never tried from other cases of studies that utilize conventional voting data and made it. We also derived co-operatives of each visualization through conducting a comparative experiment for the two visualization. As a research result, we found that Proximity based Circular Visualization can be better analysis each node and Network Graph Visualization can be better analysis patterns for the nations.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.99-105
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• 2011

We propose a semi-supervised learning algorithm based on a form of regularization that incorporates similarity and dissimilarity penalty terms. Our approach uses a graph-based encoding of similarity and dissimilarity. We also present a model-selection method which employs cross-validation techniques to choose hyperparameters which affect the performance of the proposed method. Simulations using two types of dat sets demonstrate that the proposed method is promising.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.138-141
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• 2004

본 논문은 EBGM(Elastic Bunch Graph Matching)기법을 이용한 얼굴인식에 대해 다룬다. 대용량 영상 정보에 대해 차원 축소를 이용한 얼굴인식 기법인 주성분기법이나 선형판별기법에서는 얼굴 영상 전체의 정보를 이용하는 반면 본 논문에서는 얼굴의 눈, 코, 입 등과 같은 얼굴 특징점에 대해 주파수와 방향각이 다른 여러 개의 가버 커널과 영상 이미지의 컨볼루션(Convolution)의 계수의 집합(Jets)을 이용한 특징 데이터를 이용한다. 하나의 얼굴 영상에 대해서는 모든 영상이 같은 크기의 특징 데이터로 표현되는 Face Graph가 생성되며, 얼굴인식 과정에서는 추출된 제트의 집합에 대해서 상호 유사도(Similarity)의 크기를 비교하여 얼굴인식을 수행한다. 본 논문에서는 기존의 EBGM방법의 Face Graph 생성 과정을 보다 간략화 한 방법을 이용하여 얼굴인식 과정에서 계산량을 줄여 속도를 개선하였다.