• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 춘계학술발표대회
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• pp.50-52
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• 2009

Ontology maintenance is a time consuming and costly process which requires special skill and knowledge. It requires joint effort of both ontology engineer and domain specialist to properly maintain ontology and update knowledge in it. This is specially true for medical domain which is highly specialized domain. This paper proposes a novel approach for maintenance and update of existing ontologies in a medical domain. The proposed approach is based on modified Ontology Maturing Process which was originally developed for web domain. The proposed approach provides way to populate medical ontology with new knowledge obtained from medical documents. This is achieved through use of natural language processing techniques and highly specialized medical knowledge bases such as Unified Medical Language System.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.819-832
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• 2022

Cross-domain recommender system transfers knowledge across different domains to improve the recommendation performance in a target domain that has a relatively sparse model. However, they suffer from the "negative transfer" in which transferred knowledge operates as noise. This paper proposes a novel Multiple Fusion-based Deep Cross-Domain Recommendation named MFDCR. We exploit Doc2Vec, one of the famous word embedding techniques, to fuse data user-wise and transfer knowledge across multi-domains. It alleviates the "negative transfer" problem. Additionally, we introduce a simple multi-layer perception to learn the user-item interactions and predict the possibility of preferring items by users. Extensive experiments with three domain datasets from one of the most famous services Amazon demonstrate that MFDCR outperforms recent single and cross-domain recommendation algorithms. Furthermore, experimental results show that MFDCR can address the problem of "negative transfer" and improve recommendation performance for multiple domains simultaneously. In addition, we show that our approach is efficient in extending toward more domains.

• 한국정밀공학회지
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• 제23권3호
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• pp.22-31
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• 2006

This paper introduces an approach to an ontology-based knowledge framework for product life cycle management (PLM). Participants in a product life cycle want to share comprehensive product knowledge without any ambiguity and heterogeneity. However, previous knowledge management approaches are limited in providing those aspects. Therefore, we suggest an ontology-based knowledge framework including knowledge maps, axioms and specific knowledge far domain. The bottom level, the axiom, specifies the semantics of concepts and relations of knowledge so that ambiguity of the semantics can be alleviated. The middle level is a product development knowledge map; it defines the concepts and the relations of the product domain common knowledge and guides engineers to process their engineering decisions. The middle level is then classified further into more detailed levels, such as generic product level, specific product level, product version level, and product item level for PLM. The top level is specialized knowledge fer a specific domain that gives the solution of a specific task or problem. It is classified into three knowledge types: expert knowledge, engineering function knowledge, and data-analysis-based knowledge. This proposed framework is based on ontology to accommodate a comprehensive range of unambiguous knowledge for PLM and is represented with first-order logic to maintain a uniform representation.

• Journal of Information Processing Systems
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• 제13권1호
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• pp.57-67
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• 2017

In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

• Journal of Korean Biological Nursing Science
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• 제18권3호
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• pp.160-168
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• 2016

Purpose: This study was conducted to examine the pro-environmental attitude (actual commitment domain, verbal commitment domain, affect domain), behavior to decreased exposure and knowledge of endocrine disruptors by obesity -related profiles (BMI, body fat percentage, visceral fat percentage, skeletal muscle mass percentage, waist circumference, waist-hip ratio). Methods: A cross-sectional study was conducted with 102 nursing students. Data were collected from November to December, 2015 using self-report questionnaires and physical measurements. Data were analyzed using t-test, Pearson correlation and coefficients with SPSS 18.0. Results: The study results showed that actual commitment domain of pro-environmental attitude and behavior to decreased exposure level on endocrine disruptors were significantly related to visceral fat percentage. Actual commitment domain of a pro-environmental attitude was significantly related to body fat percentage. Pro-environmental attitude was significantly related to the behavior to decreased exposure level on endocrine disruptors and knowledge thereof. Conclusion: These findings suggest that visceral fat and body fat percentages were significantly related to the actual commitment domain of a pro-environmental attitude. Therefore, a replication study is recommended to understand the connection between endocrine disruptors and obesity. In addition, developing an education program about endocrine disruptors for nursing students is recommended. In particular, a pro-environmental attitude, especially on actual commitment domain, could be involved as an education program.

• 한국문헌정보학회지
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• 제39권4호
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• pp.89-104
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• 2005

디지털 보존 주제분야를 중심으로 지식영역 비주얼화(knowledge domain visualization)를 시도하였다. 분석을 위한 데이터는 1990년부터 2005년까지의 기간 동안의 Web of Science DB를 중심으로 총 74건의 문헌을 추출하여 활용하였다. 지식영역 비주얼화를 위하여 사용한 툴은 서지DB를 중심으로 비주얼 데이터마이닝 결과를 제공하는 Java 어플리케이션인 CiteSpace이다. 분석 결과, 디지털 보존 분야의 핵심적인 지식 영역은 최신정보기술을 중심으로 한 디지털 보존전략, 정보네트워크와 보존시스템, 전자정부와 지식관리의 세 영역인 것으로 나타났다.

• 한국응용과학기술학회지
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• 제39권6호
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• pp.738-747
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• 2022

이 연구는 생태형 해양스포츠의 체육교육 적용을 위한 지식구조를 구성하는 것에 목적이 있다. 구체적인 종목으로는 배구형 게임인 비치발리볼과 바다수영이 설정되었으며 연구수행을 위하여 지식구조 분석틀을 활용하였다. 연구의 타당성을 확보하기 위하여 전문가협의를 실시하였다. 연구결과는 다음과 같다. 첫째, 2022개정 체육과 교육과정 시안에 기반한 지식구조를 마련하였다. 둘째, 생태형 해양스포츠의 체육수업 적용 기반을 마련하였다.셋째, 비치발리볼의 지식·이해 영역, 과정·기능 영역, 가치·태도 영역의 학습 내용을 제안하였다. 넷째, 바다수영의 지식·이해 영역, 과정·기능 영역, 가치·태도 영역의 학습 내용을 제안하였다. 이 연구는 향후 도입될 2022개정 체육과 교육과정의 실현된 모습을 미리 대비하였다는 것에 의미가 있다.

• 지식경영연구
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• 제6권2호
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• pp.23-47
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• 2005

This study is performed the multinomial logistic regression with the officials needs level about a component of knowledge administration for drawing a demand estimation model in the knowledge administration activities. This study is not that an activity and domain of knowledge administration is to apply and to operate uniformly it in public sector, one is suggested an application with a demand diagnose of knowledge administration in order to saw a course of the knowledge administration programs to suit a function and role of public administration. A result of this study is that an activity and domain of the knowledge administration is different from a component of it namely, knowledge creating, knowledge organizing, knowledge sharing and distribution, knowledge utility, and knowledge store. And the officials individual characteristics, administration agency, a kind of business, and a function and role of work are different from demand of knowledge administration. Also, the practical use of KMS (knowledge management system) is not so high in public sector. Accordingly, the tools of knowledge administration will deliberate on a consolidation with the existing system in the device.

• 대한안전경영과학회지
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• 제15권3호
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• pp.133-141
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• 2013

As a work analysis technique, Work Domain Analysis (WDA) aims to identify the design knowledge structure of a work domain that human operators interact with through human-system interfaces. Abstraction hierarchy (AH) is a multi-level, hierarchical knowledge representation framework for modeling the functional structure of any kinds of systems. Thus, WDA based on AH aims to identify the functional knowledge structure of a work domain. AH has been used in a range of work domains and problems to model their functional knowledge structure and has proven its generality and usefulness. However, many of researchers and system designers have reported that it is never easy to understand the concepts underlying AH and use it effectively for WDA. This would be because WDA is a form of work analysis that is different from other types of work analysis techniques such as task analysis and AH has several unique characteristics that are differentiated from other types of function analysis techniques used in systems engineering. With this issue in mind, this paper introduces the concepts of WDA based on AH and offers a comprehensive list of references. Next, this paper proposes a set of principles for effectively applying AH for work domain analysis, which are developed based on the author's experiences, consultation with experts, and literature reviews.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.173.5-173
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• 2001

Q-Learning is a most widely used reinforcement learning, which addresses the question of how an autonomous agent can learn to choose optimal actions to achieve its goal about any one problem. Q-Learning can acquire optimal control strategies from delayed rewards, even when the agent has no prior knowledge of the effects of its action in the environment. If agent has an ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. We present a novel reinforcement learning method using domain knowledge, which is represented by problem-independent features and their classifiers. Here neural network are implied as knowledge classifiers. To show that an agent using domain knowledge can have better performance than the agent with standard Q-Learner. Computer simulations are ...