• Journal of KIISE
• /
• v.44 no.2
• /
• pp.139-147
• /
• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• Genomics & Informatics
• /
• v.18 no.2
• /
• pp.18.1-18.5
• /
• 2020

Finding publications that propose alternative methods to animal experiments is an important but time-consuming task since researchers need to perform various queries to literature databases and screen many articles to assess two important aspects: the relevance of the article to the research question, and whether the article's proposed approach qualifies to being an alternative method. We are currently developing a Web application to support finding alternative methods to animal experiments. The current (under development) version of the application utilizes external tools and resources for document processing, and relies on the PubAnnotation ecosystem for annotation querying, annotation storage, dictionary-based tagging of cell lines, and annotation visualization. Currently, our two PubAnnotation repositories for discourse elements contain annotations for more than 110k PubMed documents. Further, we created an annotator for cell lines that contain more than 196k terms from Cellosaurus. Finally, we are experimenting with TextAE for annotation visualization and for user feedback.

• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.399-405
• /
• 2007

생물 의료 분야에서 정보의 양이 아주 빠르게 증가하고 있다. 이러한 방대한 양의 정보에서 유용한 정보를 추출하기 위해 텍스트 마이닝 기법을 이용한 연구들이 많이 진행되어 왔다. 그렇지만 이렇게 뽑아진 정보조차 그 양이 방대하고, 또한 텍스트로 되어 있기 때문에 직관적으로 이해하기가 어렵다. 따라서 이러한 정보들을 좀 더 직관적으로 이해하기 위해서는 정보 시각화 시스템이 필수적이다. 최근 들어 이러한 정보 시각화에 대한 연구가 많이 진행되었으나 이러한 시각화 정보조차 너무나 방대하기 때문에 사용자가 필요로 하는 정보를 여과해 주는 방법이 필요하다. 그리고 시각화 시스템에서의 지식 발견을 위한 방법을 제공하여야 한다. 본 논문에서는 생물 의료 정보의 텍스트 시각화에 초점을 맞추어 생물 의료 정보의 효과적인 표현 방법과 지식 발견을 위한 직관적인 인터페이스를 제안하고자 한다.

• Journal of Ocean Engineering and Technology
• /
• v.22 no.5
• /
• pp.126-131
• /
• 2008

The voyage data is very important for safety of ships, and duty being effectuated by the installation of voyage data record (VDR) on ships. VDR is a black-box, and it contains 14 kinds of voyage data as text. But it is not easy to understand when the accident happened because voyage data is saved as complicated texts. User interface (UI), analysis, visualization system, which works for assist to gather information about situation of accident, was developed. It will be possible to develop onboard ship monitoring and voyage prediction system by the VDR visualization system's development in near future.

• Journal of Information Technology Services
• /
• v.21 no.2
• /
• pp.85-95
• /
• 2022

This study analyzed the translation error factors of machine translation services such as Naver Papago and Google Translate through Self-Attention path visualization. Self-Attention is a key method of the Transformer and BERT NLP models and recently widely used in machine translation. We propose a method to explain translation error factors of machine translation algorithms by comparison the Self-Attention paths between ST(source text) and ST'(transformed ST) of which meaning is not changed, but the translation output is more accurate. Through this method, it is possible to gain explainability to analyze a machine translation algorithm's inside process, which is invisible like a black box. In our experiment, it was possible to explore the factors that caused translation errors by analyzing the difference in key word's attention path. The study used the XLM-RoBERTa multilingual NLP model provided by exBERT for Self-Attention visualization, and it was applied to two examples of Korean-Chinese and Korean-English translations.

• Journal of Information Technology Applications and Management
• /
• v.24 no.1
• /
• pp.33-43
• /
• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• Journal of Information Technology Applications and Management
• /
• v.26 no.3
• /
• pp.43-59
• /
• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korean new year addresses, one of most important and authoritative document publicly announced by North Korean government. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. We propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and co-occurrence networks. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of Kim Jung Un of the North Korea from 2017 to 2019. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. We found that uncovered semantic structures of North Korean new year addresses closely follow major changes in North Korean government's positions toward their own people as well as outside audience such as USA and South Korea.

• Journal of Fashion Business
• /
• v.21 no.4
• /
• pp.90-104
• /
• 2017

In a rapidly changing fashion design world, creative ideas are always required. Knowledge has been created as an art, exhibiting a new imagination that surpasses reality, while being visualized from the past. The purpose of this study is to derive types of visualization of knowledge for continuous creation of fashion design. The study consists of literature and empirical studies. ATTA, a creativity evaluation method made by Torrance applied for analyzing of Gucci fashion design collections from 2016-2017. Creativity of the Gucci collection by ATTA evaluation items are that first, a vivid idea was revealed through collection history, myths and animal and plants, second, conceptual incongruity is in the composition of the garment, third, provocative questions are the symbolism of the meaning of the text, fourth, different perspectives derive a new formative beauty through the synthesis of twisted elements, fifth, abstraction is a symbolic expression of animals and plants, sixth, synthesis is a mixture of various materials and techniques by plural inspiration, seventh, context, it is developed as a design accompanied by stories of history and myth and eighth, fantasy is fictitious animals and animals and detail. In addition, formativeness of fashion design by visualization of this knowledge was extracted with contamination, symbolism, enjoyment and fabrication. Visualization of the knowledge is expected to be used as a strategy to attract ongoing ideas for creative fashion designs.

• The Journal of Korean Association of Computer Education
• /
• v.18 no.4
• /
• pp.63-70
• /
• 2015

Program source code is a set of complex syntactic information which are expressed in text forms, and contains complex logical structures. Structural and logical complexity inside source code become barriers in applying visualization techniques shown in traditional big-data approaches when the volume of source code become over ten-thousand lines of code. This paper suggests a procedure for making visualization of structural characteristics in source code. For this purpose, this paper defines internal data structures as well as inter-procedural relationships among functions. The paper also suggests a means of outlining the structural characteristics of source code by visualizing the source codes with network forms The result of the research work can be used as a means of controling and understanding the massive volume of source code.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.5
• /
• pp.453-460
• /
• 2015

Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study, we propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and low-dimensional embedding. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of the presidents of the United States from 1789 to 2009. The main results of this study show that trends in the national policy agenda can be discovered based on clustering and visualization algorithms.