• Proceedings of the Korean Society for Language and Information Conference
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• 2002.06a
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• pp.48-67
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• 2002

This paper is a report on part of the results of a research project entitled "Research and Model Development for a Multi-Lingual Lexical Database". It Is a six-year project in which we aim to construct a model of a multilingual lexical database of Korean, Chinese, Japanese, and English. Now we have finished the first two-year stage of the project In this paper, we present the goal of the project, the construction model of items in the lexical database, and the possible (semi-)automatic methods of acquisition of lexical information. As an appendix, we present some sample items of the database as an i1lustration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.609-610
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• 2006

This paper introduces a methodology for construction of ontology-based product knowledge Map. For CPC(Collaborative Product Commerce) environment, engineering application of ontology has been studied . However, there are no generic and comprehensive methodologies for ontology construction yet because of such problems: dependency on experience of ontologist and domain experts and insufficiency of detail stages or rules. To solve those problems, we propose a methodology to construct ontology from engineering documents in semi-automatic. We use middle-out strategy and term's axioms, sub-definitions, to build ontology map. 5-turple ontology structure, semantic network and First order logic (FOL) are used for ontology definition in this study.

• Journal of the Korean Society for information Management
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• v.35 no.4
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• pp.141-164
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• 2018

Recently, as the amount of academic literature has increased rapidly and complex researches have been actively conducted, researchers have difficulty in analyzing trends in previous research. In order to solve this problem, it is necessary to classify information in units of academic papers. However, in Korea, there is no academic database in which such information is provided. In this paper, we propose an automatic classification system that can classify domestic academic literature into multiple classes. To this end, first, academic documents in the technical science field described in Korean were collected and mapped according to class 600 of the DDC by using K-Means clustering technique to construct a learning set capable of multiple classification. As a result of the construction of the training set, 63,915 documents in the Korean technical science field were established except for the values in which metadata does not exist. Using this training set, we implemented and learned the automatic classification engine of academic documents based on deep learning. Experimental results obtained by hand-built experimental set-up showed 78.32% accuracy and 72.45% F1 performance for multiple classification.

• Korean Journal of Computational Design and Engineering
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• v.16 no.4
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• pp.260-267
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• 2011

Nowadays, automobile manufacturers are faced with increasing global competition which is required low cost as well as high quality. To reduce shipping and handling cost and delivery time, lots of automobile manufactures tried to build their new factory in the neighborhood of market. Simultaneously, many factories are under construction in developing countries to make efficient use of low-wage workers. However, because systems are installed in developing countries as the same type of domestic facilities, systems have lots of problems such as high installation cost and inefficient use of manpower. To find core problems and generate optimal solution of these problems, thinking process of TOC(Theory Of Constrains) is used. In case of transmission gear machining system, semi-auto system is proposed as the best solution to increase manpower efficiency and system utilization. Semi-auto system consists of automatic machining process and manual transporting process. The system layout is generated based on semi-auto process concept. And, 3D simulation method using QUEST is used to verify production volume of generated system.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1786-1797
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• 2010

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.290-295
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• 2002

Since thesaurus is used as a knowledge resource in many natural language processing systems, it is very useful and necessary for the high quality systems, especially for dealing with semantics. In this paper, we introduce a semi-automatic method for the construction of Korean noun semantic hierarchy by utilizing a monolingual MRD and an existing thesaurus.

• Journal of Astronomy and Space Sciences
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• v.20 no.2
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• pp.143-152
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• 2003

We constructed the photometric observation system with a small semi-automatic telescope for the systematic observations of eclipse timings of eclipsing binary stars. The system is consisted of a Paramount GT-1100s mount system, a Celestron 14 optical system, and a SBIG ST-8 camera. We developed the OBSTOOL S/W which controls the telescope and the CCD camera using the COM(Component Object Model) supported by the softwares, The Sky and MaximDL. The system performs photometric observations of a variable, comparison and check stars by moving the telescope to the chosen star separately in a similar way such as the method of photoelectric observation. We wrote pert scripts which enable a data handling pipeline for the obtained data to be classified by each of date, object and filter. And thus the images are easily preprocessed using the IRAF S/W package. Eclipse light curves of some eclipsing binary stars observed with this system are presented.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.481-489
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• 2010

Due to the increase of the attention on relation extraction systems, the construction of test collections for assessing their performance has emerged as an important task. In this paper, we propose semi-automatic framework capable of constructing test collections for relation extraction on a large scale. Based on this framework, we develop a test collection which can assess the performance of various approaches to extracting relations between technical terminologies in scientific literatures. This framework can minimize the cost of constructing this kind of collections and reduce the intrinsic fluctuations which may come from the diversity in characteristics of collection developers. Furthermore, we can construct balanced and objective collections by means of controlling the selection process of seed documents and terminologies using the proposed framework.

• Journal of Information Technology Application
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• v.2 no.1
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• pp.1-24
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• 2000

Project management in the Construction field inherently has more uncertainty and more risks relative to ones from other area. This is the very reason for why project management is recognized as the important task to construction companies. For getting better performance in the project management, we need a system that keeps the consistencies in a automatic or semi-automatic manner through the project management stages like as project definition stage, project planning stage, project design and implementation stage. But since the early stages such as definition and planning stages has many unstructured features and also are dependent to unique expertise or experience of a specific company, we have difficulty providing systematic support for the task of these stages. This kind of problem becomes harder to solve especially in the plant construction domain that is our target domain. Therefore, in this paper, we propose and also implement a systematic approach to resolve the problem mentioned for the early project management stages in the plant construction domain. The results of our approach can be used not only for the purpose of the early project management stages but also can be used automatically as an input to commercial project management tools for the middle project management stages. Because of doing in this way, the construction project can be consistently managed from the definition to implementation stage in a seamless manner. For achieving this purpose, we adopt knowledge based inference, CBR, and neural network as major methodologies and we also applied our approach to two real world cases, power plant and drainage treatment plant cases from a leading construction company in Korea. Since these two application cases showed us very successful results, we can say our approach was validated successfully to the plant construction area. Finally, we believe our approach will contribute to many project management problems from more broader construction area.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1252-1255
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• 2006

본 논문은 정보검색, 정보추출, 번역, 자연어처리 등의 작업을 위한 통계적 방법론에서 필요한 학습 데이터 구축을 효율적으로 하기 위한 학습 데이터 반자동 구축 장치 및 그 방법에 대하여 기술한다. 본 논문에서는 학습 데이터 구축양을 줄이기 위해서 능동 학습을 이용한다. 또한 최근 각광 받고 있는 Conditional Random Fields(CRF)를 능동학습에 이용하기 위해서 CRF를 이용한 Confidence measure를 정의한다.