• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.3-3
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• 2003

With biomedical literature expanding so rapidly, there is an urgent need to discover and organize knowledge extracted from texts. Although factual databases contain crucial information the overwhelming amount of new knowledge remains in textual form (e.g. MEDLINE). In addition, new terms are constantly coined as the relationships linking new genes, drugs, proteins etc. As the size of biomedical literature is expanding, more systems are applying a variety of methods to automate the process of knowledge acquisition and management. In my talk, I focus on the project, GENIA, of our group at the University of Tokyo, the objective of which is to construct an information extraction system of protein - protein interaction from abstracts of MEDLINE. The talk includes (1) Techniques we use fDr named entity recognition (1-a) SOHMM (Self-organized HMM) (1-b) Maximum Entropy Model (1-c) Lexicon-based Recognizer (2) Treatment of term variants and acronym finders (3) Event extraction using a full parser (4) Linguistic resources for text mining (GENIA corpus) (4-a) Semantic Tags (4-b) Structural Annotations (4-c) Co-reference tags (4-d) GENIA ontology I will also talk about possible extension of our work that links the findings of molecular biology with clinical findings, and claim that textual based or conceptual based biology would be a viable alternative to system biology that tends to emphasize the role of simulation models in bioinformatics.

• Journal of the Korean Society for information Management
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• v.21 no.2
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• pp.249-267
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• 2004

Although MARC can define the detail cataloguing data, it has complex structures and frameworks to represent bibliographic information. On account of these idiosyncratic features of MARC, XML DTD or RDF/S that supports simple hierarchy of conceptual vocabularies cannot capture MARC formalism effectively. This study implements bibliographic ontology by means of abstracting conceptual relationships between bibliographic vocabularies of MARC. The bibliographic ontology is formalized with OWL that can represent the logical relations between conceptual elements and specify cardinality and property value restrictions. The bibliographic ontology in this study will provide metadata for cataloguing data and resolve compatibility problems between cataloguing systems. And it can also contribute the development of next generation bibliographic information system using semantic Web services.

• Korean Institute of Interior Design Journal
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• v.19 no.5
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• pp.48-55
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• 2010

Marcel Wanders, one of the greatest designers in the world of contemporary design, was born in the Netherlands. His works run the gamut from interior design to furniture design to lighting design, building a unique world of works. He started to gain fame when he presented "Knotted Chair" at Droog Design in 1996, which was made out of aramid ropes and later became his symbol. In 2000, he established "moooi," a world-renowned design label. By giving characteristic qualities, his works are given meaning, and like a fantastical dream, their images are extremely fantastical and stimulating. As can be seen in his character cover, he puts emphasis on the harmony between minimalism and decoration, establishing his own unique design concept. In this thesis, based on Marcel Wander's design philosophy, his overall design characteristics were classified into theatrical effects and storytelling. Expressive elements depaysement, eclectic mixture, and scale modification were derived from theatrical effects and analyzed; for storytelling, object, semantic cues, and dream and fantasy were derived and analyzed. A distinguishing feature of such analysis is his meaning-centric design approach, the principle by which to form long-term relationships with the users by creating user-centric designs that make them find meaning and values in diverse experiences in their daily routine, giving them familiar yet unique experience.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.207-218
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• 2012

In recent years, the skyline query paradigm has been established as a reliable method for database query personalization. While early efficiency problems have been solved by sophisticated algorithms and advanced indexing, new challenges in skyline retrieval effectiveness continuously arise. In particular, the rise of the Semantic Web and linked open data leads to personalization issues where skyline queries cannot be applied easily. We addressed the special challenges presented by linked open data in previous work; and now further extend this work, with a heuristic workflow to boost efficiency. This is necessary; because the new view on linked open data dominance has serious implications for the efficiency of the actual skyline computation, since transitivity of the dominance relationships is no longer granted. Therefore, our contributions in this paper can be summarized as: we present an intuitive skyline query paradigm to deal with linked open data; we provide an effective dominance definition, and establish its theoretical properties; we develop innovative skyline algorithms to deal with the resulting challenges; and we design efficient heuristics for the case of predicate equivalences that may often happen in linked open data. We extensively evaluate our new algorithms with respect to performance, and the enriched skyline semantics.

• Journal of Computing Science and Engineering
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• v.1 no.1
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• pp.95-123
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• 2007

Existing systems that support semistructured views do not maintain semantics during the process of designing the views. Thus, these systems do not guarantee the validity and reversibility of the views. In this paper, we propose an approach to address the issue of valid and reversible semistructured views, We design a set of view operators for designing semistructured views. These operators are select, drop, join and swap. For each operator, we develop a complete set of rules to maintain the semantics of the views. In particular, we maintain the evolution and integrity of relationships once an operator is applied. We also examine the reversible view problem under our operators and develop rules to guarantee that the designed views are reversible. Finally, we examine the changes in the participation constraints of relationship types during the view design process, and develop rules to ensure the correctness of the participation constraints.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.366-373
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• 2009

In this paper, we propose a similarity search algorithm for image databases. One of the central problems regarding content-based image retrieval (CBIR) is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms used in CBIR have used the Minkowski metric (or $L_p$-norm) to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information. Our new search algorithm tackles this problem by employing new similarity measures and ranking strategies that reflect the nonlinearity of human perception and contextual information. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.

• Korean Journal of Adult Nursing
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• v.17 no.2
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• pp.239-247
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• 2005

Purpose: The purpose of this study is to examine the relationship among body image, depression and sexual function in Korean postmenopausal women. Methods: Subjects were 96 postmenopausal women who have lived in Korea. Data was collected using Semantic Differential scale, CES-D, and FSFI. Results: The level of body image was positive, depression was mild, and sexual function was moderate. There were no significant correlation between depression and sexual function. The subjects who had more positive body image experienced higher sexual function and less depressed mood. Conclusion: These findings showed the need for a knowledge development program for nurses regarding women's sexual function. Also, nurses must do counseling with sexual partner's and consider patients' body image when counseling those who complain of sexual dysfunction.

• The Journal of Society for e-Business Studies
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• v.10 no.3
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• pp.21-36
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• 2005

Current web search includes a lot of different results with information that user does not want, because it searches information using keyword mapping. Ontology can describe the correct meaning of web resource and relationships between web resources. And we can extract suitable information that user wants using Ontology Accordingly, we need the ontology to represent knowledge. W3C announced OWL(Web Ontology Language), meaning description technology for such web resource. However, the development of a special tool that can effectively compose and edit OWL is inactive. In this paper, we designed and developed an OWL authoring system that can effectively provide the generation and edit about OWL.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.97-106
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• 2007

A core technology that may realize the Semantic Web is Ontology. The OWL (Web Ontology Language) has been positioned as a standard language. It requires technical expertise to directly represent the domain knowledge in OWL. Based on our experience of analyzing the fundamental relationships of concepts in e-catalog domain, we have developed a visual product information modeler called PROMOD. The modeling editor makes it possible to automatically generate the OWL codes for the given product information. We employ an Extended Entity-Relationship for conceptual modeling, enriched with modeling elements specialized for the product domain. In this paper, we present our translation schemes from EER model to OWL codes, and how to design and implement the modeling editor. We also provide a scenario to demonstrate the usage of the editor in practice.