• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.6
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• pp.705-709
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• 2013

We in this paper proposed a distinction method to compare objects in the Korean medicine ontology. Comparing same and different information between two objects, two scenarios to use our system were designed and implemented. One is the distinction of formulas, medicinal materials, and acupuncture points and the other is the distinction of effects, treatments, and symptoms. To compare objects easily, the decomposition of effects and treatments and the processing of medicinal materials were provided. Also, information of each object was shown through the link to the semantic search system. Our distinction method and system could be helpful to compare and distinct two objects in the diagnosis systems or search systems of Korean medicine information.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.11-20
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• 2018

The Q&A board is widely used as a means of communicating service enquiries, and the need for efficient management of the enquiry system has risen because certain questions are being repeatedly and frequently registered. This study aims to construct a student-centered FAQ, centered on the unstructured data posted on the university homepage's Q&A board. We extracted major keywords from 690 postings registered in the recent 3 years, and conducted the semantic network analysis to find the relationship between the keywords and the centrality analysis in order to carry out network visualization. The most central keywords found through the analysis, in order of centrality, were application, curriculum, credit point, completion, graduation, approval, period, major, portal, department. Also, the major keywords were classified into 8 groups of course, register, student life, scholarship, library, dormitory, IT and commute. If the most frequent questions are organized into these areas to form the FAQ, based on the results above, it is expected to contribute to user convenience and the efficiency of administration by simplifying the service enquiry process for repeated questions, as well as enabling smooth two-way communication among the members of the university.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.125-145
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• 2005

This paper presents a semantic vector space retrieval model incorporating a word sense disambiguation algorithm in an attempt to improve retrieval effectiveness. Nine Korean homonyms are selected for the sense disambiguation and retrieval experiments. The total of approximately 120,000 news articles comprise the raw test collection and 18 queries including homonyms as query words are used for the retrieval experiments. A Naive Bayes classifier and EM algorithm representing supervised and unsupervised learning algorithms respectively are used for the disambiguation process. The Naive Bayes classifier achieved $92\%$ disambiguation accuracy. while the clustering performance of the EM algorithm is $67\%$ on the average. The retrieval effectiveness of the semantic vector space model incorporating the Naive Bayes classifier showed $39.6\%$ precision achieving about $7.4\%$ improvement. However, the retrieval effectiveness of the EM algorithm-based semantic retrieval is $3\%$ lower than the baseline retrieval without disambiguation. It is worth noting that the performances of disambiguation and retrieval depend on the distribution patterns of homonyms to be disambiguated as well as the characteristics of queries.

• Korean Association of Arts Management
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• no.59
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• pp.5-31
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• 2021

The purpose of this study is to analyze social perceptions and discourses about creating arts in the era of artificial intelligence with making an implication of responding to the emergence of artificial intelligence. We conceptually understand the principles and limitations of creating visual arts using artificial intelligence whilst this paper addresses ai art in the social context by borrowing the theoretical lens from the sociology of arts. This article considers 472 newspapers about artificial intelligence art as the main data, which are interpreted through semantic network analysis. The analysis of this research shows that it is a controversial issue regarding who/which creates the artworks between humans and computers. However, judging from the dominant influence of a group of words representing the recognition of intellectual property rights, we have detected that social awareness is formed around the perspective of considering artificial intelligence creates visual arts rather than artists. In addition, based on the close relationship between the cluster and the cluster reflecting institutional support, we confirm that the discourse about artificial intelligence art is limited to technological development and legal system maintenance. Thus, this study suggests the need for defining artificial intelligence as the medium of art and constructing policy discourses on artificial intelligence art as an artistic genre.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• 한국디지털정책학회:학술대회논문집
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• 2004.11a
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• pp.389-404
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• 2004

Web services are deemed as the natural choice for deploy- ing e-government applications. Their use enables e-government to fully get advantage of the envisioned Semantic Web. In this paper, we pro- pose WebDG, a comprehensive Web Service Management System for e-government applications. It aims to improve government-citizen inter- actions through an infrastructure built around the "life experience" of citizens. WebDG provides a framework for automatically composing e- government services, optimized querying services, and preserving privacy.

• Asia pacific journal of information systems
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• v.6 no.2
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• pp.125-148
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• 1996

This paper is primarily focused on the function of model management systems such as higher level representations and buildings of optimization models using them, especially in the area of the telecommunication network models. This research attempts to provide the model builders an intuitive language-namely higher level representation-using five distinctivenesses : Objective, Node, Link, Topological Constraint including five components, and Decision. The paper elaborates all components included in each of distinctivenesses extracted from structural characteristics of typical telecommunication network models. Higher level representations represented with five distinctivenesses should be converted into base level representations which are employed for semantic representations of linear and integer programming problems in knowledge: assisted optimization modeling system(UNIK-OPT). Furthermore, for formulating the network model using higher level representations, the reasoning process is proposed. A system called UNIK-NET is developed to implement the approach proposed in this research, and the system is illustrated with an example of the network model.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.183-190
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• 1996

The purpose of this paper is to show that the solutions of the problem for the anaphor ocured in korean senstence, by means of one-direction activated chart parsing leaded by a head. This is the phenomenon frequently occured in the conversation of natural language and the part necessarily required in the construction of natural language processing system for the practical use. To solve the problem of anaphor in the korean language, we have computerized definition and the management conditions necessary in the semantic classification between the anaphor and its antecedent and index are added in the feature structure in lexicon. To deal with anaphor in parser and algorithm is proposed to solve the problem for anaphor. The range of management of pareser is extended to solve the problem for anaphor of the indeclinable parts of speech in korean occured in all the sentences the parser HPSG developed previously manages.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.1-11
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• 2009

This study presents HCWS algorithm for researcher grouping on a weighted researcher network. The weights represent intensity of connections among researchers based on the number of co-authors and the number of co-authored research papers. To confirm the validity of the proposed technique, this study conducted an experimentation on about 80 research papers. As a consequence, it is proved that HCWS algorithm is able to bring about more realistic clustering compared with HCS algorithm which presents semantic relations among researchers in simple connections. In addition, it is found that HCWS algorithm can address the problems of existing HCS algorithm; researchers are disconnected since their connections are classified as weak even though they are strong, and vise versa. The technique described in this research paper can be applied to efficiently establish social networks of researchers considering relations such as collaboration histories among researchers or to create communities of researchers.