• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.343-351
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• 2001

World practice is evidence of that computer systems of an intellectual support of medical activities bound up with examination of patients, their diagnosis, therapy and so on are the most effective means for attainment of a high level of physician\`s qualification. Such systems must contain large knowledge bases consistent with the modern level of science and practice. To from large knowledge bases for such systems it is necessary to have a medical ontology model reflecting contemporary notions of medicine. This paper presents a description of an observation ontology, knowledge base for the physician of general tipe, architecture, functions and implementation of problem independent shell of the system for intellectual supporting patient examination and mathematical model of the dialog. The system can be used by the following specialist: therapeutist, surgeon, gynecologist, urologist, otolaryngologist, ophthalmologist, endocrinologist, neuropathologist and immunologist. The system supports a high level of examination of patients, delivers doctors from routine work upon filling in case records and also automatically forms a computer archives of case records. The archives can be used for any statistical data processing, for producing accounts and also for debugging of knowledge bases of expert systems. Besides that, the system can be used for rise of medical education level of students, doctors in internship, staff physicians and postgraduate students.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Fashion & Textile Research Journal
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• v.11 no.4
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• pp.648-660
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• 2009

In this research, an expert system has been developed to select optimum well-matched fusible interlinings with a face fabric. First, a database of face fabrics and fusible interlinings has been constructed. And knowledge acquisition has been performed from the previous studies about the properties of fusible interlinings and fused composites as well as fusing prsocess quality control. Then, a rule-based knowledge-base has been constructed through knowledge classification. Finally, we have constructed an inference engine with the knowledge-base. The expert system enables us to easily select optimum fusible interlinings for a face fabric considering high quality fused composites and fashion trend.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.173-193
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• 2016

Start-ups do not have enough resources such as financial capital and established customer base. Knowledge base of start-up team members is the crucial and unrivaled resource. This study tries to explicate the knowledge sharing process occurring in this knowledge base of start-up teams. Adopting the knowledge process view, detail process of knowledge sharing process model is constructed consisting of knowledge sharing speed, quality and quantity in a form of nomological net and empirically tested. In addition, preceding antecedents and consequential outcomes of this knowledge sharing is also posited as part of the research model : knowledge state of the team leading to team creativity and agility via the explicated knowledge sharing process model. Also, as this knowledge based view are triggered by the advance of IT in general, IT support is conceptualized as an antecedent and measures are operationalized. 230 data points were collected from start-up teams. Via data analysis using PLS, theoretical relationships from knowledge state, IT support, knowledge sharing process and knowledge consequences are found to be empirically supported except a few not supported. Most of all, team agility and team creativity are theoretically supported and empirically validated as critical outcome variables beyond performance measures. Though agility and creativity has been discussed as critical construct in start-up teams, it has not be much validated empirically. Also, interestingly, IT support are found to be significantly impacting the knowledge sharing process as expected. Academic contributions and implications for practice are discussed at the end with limitations and further research.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.452-457
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• 2015

In this paper, we propose a spatial query processor, SQUERY, which can derive rich query results through spatial reasoning on the initial knowledge base, as well as, process both qualitative and quantitative queries about the topological and directional relationships between spatial objects. In order to derive richer query results, the query processor expands the knowledge base by applying forward spatial reasoning into the initial knowledge base in a preprocessing step. The proposed query processor uses not only qualitative spatial knowledge describing topological/directional relationship between spatial objects, but also utilizes quantitative spatial knowledge including geometric data of individual spatial objects through geometric computation. The results of an experiment with the OSM(Open Street Map) spatial knowledge base demonstrates the high performance of our spatial query processing system.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2075-2082
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• 1998

Rule-based knowledge representHtion is, the most popular technique for ,storage and manipulation of domain knowledge in expert system. By the way, the amount of knowledge increases more and more in this representatiun technique, it, relationship becomes complex, and even its contents can be modified. This is the reason why rule-based knowledge representation technique requires a verification ,system which can maintain consistency and completeness of knowledge base. This paper is to propose a verification system for consistency and completeness of knowledge base to promote the efficiency and reliability of expert system. After verifying the potential errors both in structure and in semantics whenever a new rule is added, this system renders knowledge base consistent and complete by correcting them automatically or by making expert correct them if it fails.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.720-728
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• 1990

A learning control method is proposed in this paper, using a knowledge base which contains control rules, data, and patterns of the past experience of a plant. The knowledge for plant control is retrieved from measurement data during operation and continually modified after control performance evaluation. A control method is proposed using tinually modified after control performance evaluation. A control method is proposed using fuzzy model of the plant and a recursive statistic decision method of fuzzy subset for control rule generation. Also, the resulting knowledge-based control algorithm has been applied to aprocess and its performance improvement and proper generation of appropriate control rules have been verified.

• Journal of the Korean School Mathematics Society
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• v.11 no.4
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• pp.595-608
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• 2008

The purpose of this study is to address the teachers' knowledge bases for effective mathematics teaching and especially to provide the various definitions and the structures of mathematics knowledge which is the most important one of the knowledge bases. The conceptual understanding about teachers' knowledge bases for effective mathematics teaching and the structure of mathematics knowledge may be used in evaluating effective mathematics teaching and teachers as well as in developing a new conceptual framework for the structure of mathematical knowledge.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1209-1231
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• 2011

This study attempts to develop an analysis method on future skills needs for emerging new knowledge on the basis of technology outlook and patent analysis. It will extract knowledge base of patent classification for secondary battery, and will demonstrate that the density and centrality of knowledge base can be changed according to the paradigm shift of battery industry. Furthermore, on the basis of that, it will execute diagnosis on current related curriculum and present necessary improvement items of curriculum. This study tries not only to raise the utilizing level of patent analysis by demonstrating how patent analysis is used in future skills needs analysis, but also to advance the analysis method for future skills needs.

• 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.