• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.842-851
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• 2000

Developers can reuse not only class code but also wide range of knowledge on domain by reusing framework. Existing Object-Oriented Methodology and Catalysis Methodology were presented when redefining component in the course of redesigning framework. However, existing methodologies have weakness that entire process is waterfall mode or design of interface lays too much stress on implementation stage. So, this thesis will present Component-Oriented Methodology for the reuse of framework, and construct the environment for framework and domain development. That is, domain is analyzed by input of domain knowledge on real world to create software based on component, and hotspot is identified through analyzed information, and refactoring by putting additional information on users and developers. After that, I will create domain framework and application framework depending on domain. In this Component-Oriented Methodology, information is searched, understood and extracted or composite through component library storage internally. Then this information is classified into the information on component, and used as additional information in redesigning. With this, developer can obtain reusability, easiness and portability by constructing infrastructure environment that allows to register, update and delete component through Component Management System(CMS) under he development environment which can be easily applied to his own application using framework component, in this thesis, CoRBA(Common Object Request Broker Architecture) environment.

• Journal of muscle and joint health
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• v.28 no.3
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• pp.252-262
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• 2021

Purpose: To define the concept of digital health literacy and identify its attributes. Methods: Walker and Avant's approach was employed for concept analysis. Attributes, antecedents, consequences, and the definition of digital health literacy were derived from a review of 28 studies. Results: Digital health literacy was identified to possess the following five attributes: health information seeking, health information processing, health information communication, health-related knowledge translation, and utilizing digital technology. Basic literacy skills, health concerns, motivation to use technology for health information, and access to digital technologies were all antecedents of the concept. The consequences of the concept were health behaviors, patient engagement, health status, and quality of life. Digital health literacy is the ability to seek relevant health information utilizing digital technology to solve health problems and improve quality of life. Furthermore, it refers to the translation of health-related knowledge obtained through health information processing-finding, understanding, and evaluating health information and health information communication-into the context in which individual and social factors interact. Conclusion: This study presented a new definition of digital health literacy that goes beyond existing internet-based eHealth literacy, by incorporating the context of emerging digital technologies. This proposed definition can serve as a foundation for the development of instruments and educational programs to improve individuals' digital health literacy.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.525-534
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• 2006

Ontology specifies the knowledge in a specific domain and defines the concepts of knowledge and the relationships between concepts. It is possible to provide the service based on the semantic web through the ontology. Therefore, to specify and define the knowledge in a specific domain, it is required to generate the ontology which conceptualizes the knowledge. Accordingly, to search the information of potential customers for home-delivery marketing of post office, we design the specific domain to generate the ontology based on the semantic web in this paper. And we propose how to retrieve the information, using the generated ontology. We implement the data search robot which collects the information based on the generated ontology. Also, we confirm that the ontology and the search robot perform the information retrieval exactly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.416-420
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• 1997

다양한 응용 목적의 대규모 실용적 언어지식 구축을 위해서는 한국어의 모든 언어현상을 수용할 수 있는 이상적인 언어지식(optimal linguistic knowledge) 획득을 목표로 연구해 나가야 한다. 본 연구에서 언어지식의 획득은 주어진 말뭉치의 분석을 통해 이루어진다. 주어진 말뭉치에서 새로운 언어현상이 발견되었을 경우, 기존의 언어지식은 새로운 언어현상을 수용할 뿐만 아니라 기존에 발견되었던 언어현상도 함께 수용할 수 있도록 바뀌어져야 한다. 이러한 변화의 원칙이 보장되어야만 언어지식의 양적 확장과 함께 질적 확장을 이룰 수 있다. 본 연구에서는 언어지식의 질적 확장을 언어지식의 수렴성이라고 정의하고 수렴성 보장을 위한 방법론을 연구한다. 수렴성 보장을 위해서는 먼저 언어지식 획득과정이 공정화, 자동화되어야 하고 언어지식이 변화할 때 수렴을 확인하는 과정이 필요하다. 수렴을 확인하기 위하여 구문구조 데이터베이스와 역사전(Inverted Dictionary)을 이용하는 방법을 제안한다. 지금까지는 언어지식의 양적 확장에만 치중해 왔으나 본 연구에서 제안된 방법으로 언어지식이 구축된다면 질적 확장도 함께 도모할 수 있을 것으로 기대된다.

• Korean Journal of Child Studies
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• v.31 no.6
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• pp.15-34
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• 2010

The present study examines the sex differences in 5-year-old preschoolers' aggression according to the type of aggression (overt, relational) and the effect of components of social information processing (SIP : interpretation, goal clarification, response generation, response evaluation) and emotional factors (emotionality, emotional knowledge, emotion regulation) on their aggression. The subjects were 112 5-year-olds (56 boys, 56 girls) and their 11 teachers recruited from 9 day-care centers in Seoul and Kyung-Ki province. Each child's SIP and emotional knowledge were individually assessed with pictorial tasks and teachers reported on children's aggression, emotionality, and emotion regulation by questionnaires. Results indicated that there was a significant sex difference only in the preschoolers' overt aggression. Overtly aggressive response generation in SIP was the strongest predictor of preschoolers' overt aggression while anger of negative emotionality in emotional factors was the strongest predictor of preschoolers' relational aggression.

• International Commerce and Information Review
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• v.12 no.2
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• pp.325-355
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• 2010

Knowledge-based exporting companies in Korea have reached a stage to develop new technological innovation and to pioneer new markets. But, developing new technologies and launching new products require an enormous sum of money for Research and Development(R&D) and there is still uncertainty in technological development and markets. Therefore, through open technological innovation, they are encouraged to actively use external technological sources and ideas. They also need to enhance the efficiency of the relatively little R&D investment. In this paper, firstly, it conducts a precedent study on the concept and influence factors of knowledge-based exporting companies and open technological innovation. Secondly, it sets a study model and estimates a regression coefficient to analyze the influence factors of open technological innovation of knowledge-based exporting companies which are using external resources on the process of innovation. Through case study and empirical analysis, we are going to find the implication of open technological innovation and prepare the way of the innovation for the knowledge-based exporting companies. According to the empirical analysis, variables such as firm size, processing degree, product life, patent registration, maintaining internal security didn't have positive effects on open innovation performance. On the other hand, research capability and market preoccupancy had positive effects. Therefore, to succeed in open innovation, knowledge-based exporting companies not only need to secure research capability through open innovation, but also need to preoccupy the market through commercialization of developed product.