• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.133-142
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• 2003

The major challenge for post-genomic study is to identify structural similarity and relationships of proteins. SCOP (Structural Classification of Proteins) is a typical database for this purpose, providing a derailed description of the structural and functional relationships of the proteins whose three-dimensional structures have been determined. Unfortunately, since the SCOP data is only available as a plain text format, it is cumbersome and error-prone to develop tools and resources to utilize the data more effectively. To meet these researchers to utilize the data more effectively. To meet these requirements, we have developed an XML representation for the SCOP site, users of the tool, named, SCOPBrowser, for effective search of SCOP database. In addition to the information available from the SCOP site, users of the tool can obtain various information such as viewing the tree hierarchy of structure classification of proteins, searching into whole protein domains, showing XML contents of a specific domain, and some useful statistics about protein structures.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.101-106
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• 2016

A number of non-structured data associated with lectures in the field of university education have been generated and it is an important consideration of the students's written comments lecture evaluation. The purpose of this study is to find student interaction factors associated with the student evaluation of teaching at universities, and to provide some insights into improving the student evaluation program based on the results. So, this study consists of three steps that create interaction score, collect student's written comments satisfaction, and analyze an individual professor score. There are a number of limitations to this study. The limitation is that the study was conducted on a narrow sample of the overall student population.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.129-129
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• 2017

In planning and evaluating government R&D programs, one of the first steps is to understand the government's current R&D investment portfolio - which fields or topics the government is now investing in in R&D. Analysis methods of an investment portfolio of government R&D tend traditionally to rely on keyword searches or ad-hoc two-dimensional classifications. The main drawback of these approaches is their limited ability to account for the characteristics of the whole government investment in R&D and the role of individual R&D program in it, which tends to depend on the relationship with other programs. This paper suggests a new method for mapping and analyzing government investment in R&D using a combination of methods from natural language processing (NLP) and network analysis. The NLP enables us to build a network of government R&D programs whose links are defined as similarity in R&D topics. Then methods from network analysis show the characteristics of government investment in R&D, including major investment fields, unexplored topics, and key R&D programs which play a role like a hub or a bridge in the network of R&D programs, which are difficult to be identified by conventional methods. These insights can be utilized in planning a new R&D program, in reviewing its proposal, or in evaluating the performance of R&D programs. The utilized (filtered) Korean text corpus consists of hundreds of R&D program descriptions in the budget requests for fiscal year 2017 submitted by government departments to the Korean Ministry of Strategy and Finance.

• Journal of Korean Elementary Science Education
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• v.22 no.1
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• pp.109-120
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• 2003

The purpose of this study is to observe, to analyze of the preservice teachers' naive theories about the change of season. And it is to find a instruction strategy which can solve problem about this. The general idea about the change of season is observed by the 3 methods which are simply explaining with words, explaining with pictures and models. The author is to find the similarity. difference and relationship which the preservice teachers have about the general idea about the change of season. The important changable primary factors, which can effect to the general Idea formation, are naturally dragged out through the observation of preservice teachers participation. For this study, 4 first year preservice teachers of one of national university of education are used. Before the interview. the author tries to form rapport with the preservice teachers. Experiment materials, pencil. paper, camcorder, digital recorder and interview note were used for the study with reflection of them just way they are. As the result of the interview. all of 4 preservice teachers had not being understand the concept about the change of season and the three ways of explanation methods were not matched each other, so it is revealed that the general Idea of the change of season, which the preservice teachers have, is not strongly formed. In spite of the repeated study of the change of season from elementary school to university, it has many problem about recognition of the general idea about the change of season which pre-elementary teachers have. Therefore it is needed to improve the experiment in elementary science text book and naive theories by the activity which is explaining the change of season in three dimension space. to prevent the naive theories which the preservice teachers may have.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2270-2277
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• 2016

In modern society, creation and distribution of multimedia contents is being actively conducted. These multimedia information have come out the enormous amount daily, the amount of data is also large enough it can't be compared with past text information. Since it has been increased for a need of the method to efficiently store multimedia information and to easily search the information, various methods associated therewith have been actively studied. In particular, image search methods for finding what you want from the video database or multiple sequential images, have attracted attention as a new field of image processing. Image retrieval method to be implemented in this paper, utilizes the attribute of corner patches based on the corner points of the object, for providing a new method of efficient and robust image search. After detecting the edge of the object within the image, the straight lines using a Hough transformation is extracted. A corner patches is formed by defining the extracted intersection of the straight line as a corner point. After configuring the feature vectors with patches rearranged, the similarity between images in the database is measured. Finally, for an accurate comparison between the proposed algorithm and existing algorithms, the recall precision rate, which has been widely used in content-based image retrieval was used to measure the performance evaluation. For the image used in the experiment, it was confirmed that the image is detected more accurately in the proposed method than the conventional image retrieval methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.25-32
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• 2020

Data on patent and scientific paper is considered as a useful information source for analyzing technological information and has been widely utilized. Technology big data is analyzed in various ways to identify the latest technological trends and predict future promising technologies. Clustering is one of the ways to discover new features by creating groups from technology big data. Patent includes refined bibliographic information such as patent classification code whereas scientific paper does not have appropriate bibliographic information for clustering. This research proposes a new approach for clustering data of scientific paper by utilizing reference titles in each scientific paper. In this approach, the reference titles are considered as textual information because each reference consists of the title of the paper that represents the core content of the paper. We collected the scientific paper data, extracted the title of the reference, and conducted clustering by measuring the text-based similarity. The results from the proposed approach are compared with the results using existing methodologies that one is the approach utilizing textual information from titles and abstracts and the other one is a citation-based approach. The suggested approach in this paper shows statistically significant difference compared to the existing approaches and it shows better clustering performance. The proposed approach will be considered as a useful method for clustering scientific papers.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.529-536
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• 2004

The main idea of this paper is to combine position information in sentence and query type classification to make the documents ranking to query more accessible. First, the use of conceptual graphs for the representation of document contents In information retrieval is discussed. The method is based on well-known strategies of text comparison, such as Dice Coefficient, with position-based weighted term. Second, we introduce a method for learning query type classification that improves the ability to retrieve answers to questions from Question Answering system. Proposed methods employ naive bayes classification in machine learning fields. And, we used a collection of approximately 30,000 question-answer pairs for training, obtained from Frequently Asked Question(FAQ) files on various subjects. The evaluation on a set of queries from international TREC-9 question answering track shows that the method with machine learning outperforms the underline other systems in TREC-9 (0.29 for mean reciprocal rank and 55.1% for precision).

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.725-730
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• 2006

Recently Web services have become a key technology which is indispensable for e-business. Due to its ability to provide the desired information or service regardless of time and place, integrating current application systems within a single business or between multiple businesses with standardized technologies are realized using the open network and Internet. However, the current Web Services Retrieval Systems, based on text oriented search are incapable of providing reliable search results by perceiving the similarity or interrelation between the various terms. Currently there are no web services retrieval models containing such semantic web functions. This research work is purported for solving such problems by designing and implementing an extended Semantic Web Services Retrieval Model that is capable of searching for general web documents, UDDI and semantic web documents. Execution result is proposed in this paper and its efficiency and accuracy are verified through it.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1172-1181
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• 2009

An electronic medical record (EMR) is the medical system that all the test are recorded as text data. However, domestic EMR systems have various forms of medical records. There are a lot of related works to standardize the laboratory codes as a LOINC (Logical Observation Identifiers Names and Code). However the existing researches resolve the problem manually. The manual process does not work when the size of data is enormous. The paper proposes a novel automatic LOINC mapping algorithm which uses indexing techniques and semantic similarity analysis of medical information. They use file system which is not proper to enormous medical data. We designed and implemented mapping algorithm for standardization laboratory codes in medical informatics compared with the existing researches that are only proposed algorithms. The automatic creation of searching words is being possible. Moreover, the paper implemented medical searching framework based on database system that is considered large size of medical data.

• Journal of Information Management
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• v.43 no.2
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• pp.43-66
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• 2012

The purpose of this study is to see the intellectual structure in the field of veterinary sciences in Korea, using author profiling analysis(APA), a bibliometric approach. Three journals are selected on the basis of citation data, exchanging most citations with Korean Journal of Veterinary. And then, 50 authors who published most articles at selected journals during the given period of time were chosen. The analysis of similarity and dissimilarity among authors by comparing co-word appearance patterns from article title, abstracts, and keywords was made. Authors can be grouped 11 minor clusters under 4 major clusters, depending on their interests in the area of veterinary sciences in Korea. The subjects for each cluster at the veterinary sciences are decided by the matching the keyword, representing author's research interest. As a result, it is possible to figure out the current research trends and the researcher network in the field of veterinary sciences.