• Journal of KIISE
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• v.42 no.8
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• pp.1065-1070
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• 2015

The performance of many natural language processing applications depends on the knowledge base as a major resource. WordNet, YAGO, Cyc, and BabelNet have been extensively used as knowledge bases in English. In this paper, we propose a method to construct a YAGO-style knowledge base automatically for Korean (hereafter, K-YAGO) from Wikipedia and YAGO. The proposed system constructs an initial K-YAGO simply by matching YAGO to info-boxes in Wikipedia. Then, the initial K-YAGO is expanded through the use of a machine learning technique. Experiments with the initial K-YAGO shows that the proposed system has a precision of 0.9642. In the experiments with the expanded part of K-YAGO, an accuracy of 0.9468 was achieved with an average macro F1-measure of 0.7596.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.15-24
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• 2000

An intelligent software system which can support efficiently and systematically machine tool design by utilizing deign knowledge is described in this paper. The process of embodiment design of a machining center was modelled represented by IDEF0 and embedded in the system. A hybrid type inference engine has been introduced so that the system can effec-tively deal with knowledge represented in diversified forms The design system was developed on the basis of object-ori-ented programming and has been coded into one software system which can be ported on Windows NT.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.199-202
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• 2000

In this paper, we developed an expert system that helped a machine-tool designer to select a moderate servomotor of the feed drive unit of a machine tool. With the specification of the drive unit, it searches for the set of servo-motors that have enough torque and power to satisfy the driving condition which the designer defines from the database. me database also contains knowledge and rules, which describe the design process and calculate design parameters of a feed drive. The knowledge-based design support module shows every steps of inference and reason for the solution that it provides.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.146-158
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• 2023

Smart City is gaining attention with the advancement of Information and Communication Technology (ICT). ICT provides the basis for smart city foundation; enables us to interconnect all the actors of a smart city by supporting the provision of seamless ubiquitous services and Internet of Things. On the other hand, Crowdsourcing has the ability to enable citizens to participate in social and economic development of the city and share their contribution and knowledge while increasing their socio-economic welfare. This paper proposed a hybrid model which is a compound of human computation, machine computation and citizen crowds. This proposed hybrid model uses knowledge-based crowdsourcing that captures collaborative and collective intelligence from the citizen crowds to form democratic knowledge space, which provision solutions in areas of civic innovations. This paper also proposed knowledge-based crowdsourcing framework which manages knowledge activities in the form of human computation tasks and eliminates the complexity of human computation task creation, execution, refinement, quality control and manage knowledge space. The knowledge activities in the form of human computation tasks provide support to existing crowdsourcing system to align their task execution order optimally.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.761-774
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• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.06a
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• pp.1-18
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• 2000

This paper describes firstly a prediction for desirable features of the machine tool in the year 2015 and beyond, and then delineates something definite in relation to some representative machine tools, which could be realised in very near future. The paper depicts furthermore another aspect of future machine tools, I. e., innovative structural designs. In addition, author asserts the importance of grass root-like knowledge, when predicting the desirable feature of machine tools future together with showing some evidences.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.1-10
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• 2021

Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.

• The Korean Journal of Community Living Science
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• v.22 no.1
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• pp.131-143
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• 2011

The purpose of this study was to investigate i) the current state of washing knowledge and washing habits ii) the effect of washing behaviors on washing satisfaction. A survey questionnaire was developed and implemented to married women in their 30's or 40's. A total of 210 responses were analyzed by frequency analysis, t-test and ANOVA with PASW18.0. The results are as follows. First, the ways in which women do the laundry was analyzed. Married women in their 30s and 40s usually do the laundry at home and are familiar with washing symbols and do not rely on common sense. Most of them like to rely on the washing machine guidelines for detergent concentration and use the right amount for environmental protection. They also adjust the washing machine setting effectively considering laundry time, water temperature and care label. Second, the groups divided by demographic variables showed meaningful results about washing knowledge. Particularly, there is no significant difference on washing knowledge between housewives and career women. Third, the groups divided by the levels of washing habits showed suggestive results about washing satisfaction. The groups who have correct washing habits generally a indicated high degree of washing satisfaction. Finally, marketing implications for the businesses of laundry, laundromat and the manufacturers of washing machine are suggested.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.112-122
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• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.345-346
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• 2006

In the future, a machine will be more improved in the form of advanced concept with collaborative ability in M2M(Machine to Machine, Mobile to Machine) environment for u-Manufacturing system. This paper tried to standardize M2M and design advanced concept machine. The M2M is front-end system for implementing autonomous ubiquitous environment. The advanced machine in M2M will be a collaborative machine with knowledge-evolutionary ability such as u-Machine(Ubiquitous machine), Vortal(Vertical Portal) machine and P2P(Peer to Peer) machine. Such advanced concept machines will be the key subject for M2M cooperation.