• Journal of Contemporary Eastern Asia
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• v.18 no.1
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• pp.175-188
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• 2019

This study investigates public receptivity to celebrity's climate change advocacy on YouTube through a semantic network analysis. The results of this study suggest that the YouTube video generated a number of viewers' responses. Celebrity endorsement not only leaded public voices on climate change issue, but also their opinions on the celebrity endorser. This study found that most of viewers were polarized in their judgment and attitude toward the celebrity advocate either positively or negatively. This study offers an exploratory examination of the perceived star power and the role of celebrities as spokespersons for social causes. This study contributes to the theoretical foundation of the role of celebrity advocacy using social media. In addition, this study offers methodological insights into how to detect public perceptions and attitudes toward celebrity endorsement of social causes by analyzing public comments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.60-79
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• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.332-337
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• 2023

The purpose of the study was to analyze consumers' perceptions of gamification. Based on the analyzed data, we would like to provide data by systematically organizing the concept, game elements, and mechanisms of gamification. Recently, gamification can be easily found around medical care, corporate marketing, and education. This study collected keywords from social media portal sites Naver, Daum, and Google from 2018 to 2023 using TEXTOM, a social media analysis tool. In this study, data were analyzed using text mining, semantic network analysis, and CONCOR analysis methods. Based on the collected data, we looked at the relevance and clusters related to gamification. The clusters were divided into a total of four clusters: 'Awareness of Gamification', 'Gamification Program', 'Future Technology of Gamification', and 'Use of Gamification'. Through social media analysis, we want to investigate and identify consumers' perceptions of gamification use, and check market and consumer perceptions to make up for the shortcomings. Through this, we intend to develop a plan to utilize gamification.

• Asia Marketing Journal
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• v.26 no.1
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• pp.45-59
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• 2024

Extended Reality (XR) technology, the umbrella term covering hyper-realistic technologies, is known to enhance consumer experience and is therefore developing rapidly and being utilized across various industries. Growing studies have examined XR technology and consumer experience; however, the literature has failed to fully explore hyper-realistic technology through a holistic perspective. To fill this gap, we analyzed 720 Korean and international articles through semantic network analysis and topic modeling and identified the literature on XR research in consumer experience. As a result, we extracted six main topics: "Tourism," "Buying Behavior," "XR Technology Acceptance," "Virtual Space," "Game," and "XR Environment." The results provide comprehensive insight on XR technology in consumer experience, whereas the literature is bounded on the production side as revealing a lack of academic discourse on consumer rights and responsibilities. Research reflecting the consumer welfare perspective is, therefore, recommended for future studies.

• Journal of KIISE
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• v.44 no.4
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• pp.383-391
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• 2017

As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.

• Korean Journal of Cognitive Science
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• v.26 no.4
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• pp.377-392
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• 2015

Korean semantic role labeling (SRL) is usually performed by a machine learning and requires a lot of corpus. However, the Korean PropBank used in Korean SRL system is less than PropBank. It leads to a low performance. Therefore, we expand the annotated corpus and verb frames for Korean SRL system to expand the Korean PropBank corpus. Most of the SRL system have a domain-dependent performance so, the performance may decrease if domain was changed. In this paper, we use the domain adaptation technique to reduce decreasing performance with the existing corpus and the small size of new domain corpus. We apply the domain adaptation technique to Structural SVM and Deep Neural Network. The experimental result show the effectiveness of the domain adaptation technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.662-667
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• 2022

Semantic role labeling task(SRL) is to extract predicate and arguments such as agent, patient, place, time. In the previously SRL task studies, a pipeline method extracting linguistic features of sentence has been proposed, but in this method, errors of each extraction work in the pipeline affect semantic role labeling performance. Therefore, methods using End-to-End neural network model have recently been proposed. In this paper, we propose a neural network model using the Biaffine Average Attention model for SRL task. The proposed model consists of a structure that can focus on the entire sentence information regardless of the distance between the predicate in the sentence and the arguments, instead of LSTM model that uses the surrounding information for prediction of a specific token proposed in the previous studies. For evaluation, we used F1 scores to compare two models based BERT model that proposed in existing studies using F1 scores, and found that 76.21% performance was higher than comparison models.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.180-191
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• 2016

This study utilized a semantic network analysis for Korean major newspaper articles concerning 'Bernie Sanders'. 'Bernie Sanders' promotes conservative values of 'Americana' as well as the progressive values of 'relieving inequality', and thus, perhaps he is a subject on which ideological differences between the press can be distinctively manifest. Upon comparison of the priority of frequency between the conservative press and progressive press, the conservative press frequently used the expressions, 'socialist' and 'black man', whereas the progressive press frequently used the expressions, 'inequality' and 'problem'. Both the conservative press and progressive press displayed particularly different semantic compositions with the term, 'Korea'. The progressive press aimed to express the criticism of social problems and established politics identified by Sanders in relation to the 'Korean' society, whereas the conservative press criticized the blunt expressions stating that a specifically named politician resembles Sanders, and the specific party and term of 'Korea'. A completely different disposition of reports from different perspectives and context was ascertained, regardless of the use of the same terms. Thus, it is demonstrated that the semantic composition of the press on a specific issue displays significant differences according to their ideological disposition.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.70-81
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• 2009

Ontology is an explicit specification of concepts and relationships between concepts in an interest domain. As considered as one of typical knowledge representation methods, ontology is applied to various studies such as information extraction, information integration, information sharing, or knowledge management. In IT based industries, ontology is applied to research on information integration and sharing in order to enhance interoperability between enterprises. In supply chains or logistics, several enterprises participate as business partners to plan movements of goods, and control goods and logistics flows. A number of researches on information integration and sharing for the effective and efficient management of logistics or supply chains have been addressed. In this paper, we address an ontology as a knowledge-base for semantic-based integration of logistics information distributed in the logistics flow. Especially, we focus on developing an ontology that enables to represent and translate semantic meaning of EPC data in the EPC Network applied logistics. We present a scenario for tracing products in logistics in order to show the value of our ontology.

• Korean Association of Arts Management
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• no.58
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• pp.205-239
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• 2021

This study explores the importance of semantic web-based network construction in art data archiving, as well as its meaning and value in the context of arts management along with its potential for future application. The study focuses on oral history obtained from the Arko Arts Archives that contained records of the lives and artistic views of early artists. In this study, the possibility of applying semantic web-based technology to materials concerning culture and the arts was discussed in five aspects based on the results of the case analysis. First, checking the relationship and discovering hidden artists are possible by revealing relationships between characters. Second, understanding and studying society and culture at a given time is possible by interpreting the contextual meaning of information. Third, art exploration can be done broadly and deeply, encompassing various genres from the perspective of the consumer. Fourth, through art construction, history can be reconstructed using a new and rich method. Fifth, expanding the scope beyond the boundaries of art is possible through convergence and collaboration of programs that handle big data. The network data can be used in various methods, such as art history research, art planning, and creation, throughout the art ecosystem. The results of the study suggest that digitizing a large quantity of data concerning culture and the arts is meaningful in arts management as well as identifying and analyzing the relationship network among data clusters using semantic web-based technology.