• Knowledge Management Research
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• v.23 no.2
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• pp.123-142
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• 2022

The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.

• School Mathematics
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• v.6 no.2
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• pp.153-179
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• 2004

In this article, we classify the middleschool students' mathematical modeling activities with three types as following: perceptive activity, cognitive activity, and metacognitive activity. And we research model development process through the interaction of perceptive, cognitive, and metacognitive activities. We report three results of our case study as following: First, students understanded the context of the modeling tasks on the base of their own experience and constructed the tasks with perceptive activity operating tool. Second, students developed various models with reorganizing cognitive activity which think and reason about perceptive activity-based model. Third, students were able to create generalizable and reusable models through metacognitive activities. This study revealed that the possible contribution of modeling activity as following. Students are able to understand abstractive mathematical knowledge as connecting between realistic activity and abstractive activity.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.289-291
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• 2005

XML의 출현으로 메타데이터를 이용한 개념수준의 지식 모델링 토대가 구축됨으로써, 추론 기능을 부가하여 실질적인 개념화를 실현하는 온톨로지 기술이 정보시스템의 핵심요소로 부각되고 있다. 온톨로지를 적용함으로써 기존의 정보처리를 지식 처리로 고도화할 수 있으며, 다양한 지식처리 기능을 실현해 낼 수 있다. 본 연구에서는 기업이나 기 관 간의 지식 자원 관리 및 공유, 협업 등이 가능한 JSR 168과 WSRP 기반의 포탈 프레임워크를 구축하고 온톨로지 기술을 응용하여 포틀릿 정보를 의미 수준에서 관리하는 방법을 제시한다. 본 논문의 온톨로지 기반의 로틀릿 관리 기능을 갖은 엔터프라이즈 포털 시스템은 깁업의 정보 자산 관리와 정보 서비스 향상에 기반 시스템으로 활용될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.97-99
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• 2004

현재의 웹 환경에서의 정보는 점점 대량화되고 있으며, 정보에 대한 의미처리가 지원되지 않는 까닭에 많은 양의 정보가 무분별하게 검색되고 필요한 정보를 찾는데 많은 노력이 필요하다. 이를 해결하고자 XML의 의미태그를 중심으로 한 메타데이터 정보 모델링 등이 출현하였고, 이를 개념 수준의 의미처리로 추상화한 온톨로지(ontology) 기술이 개발되게 되었다. 온톨로지는 컴퓨터가 처리할 수 있는 명시적인 개념 표현을 상호 공유할 수 있도록 하여 줌으로써, 컴퓨터가 의미를 이해하고 추론할 수 있는 기반을 제공한다. 최근에는 여러 온톨로지 언어는 기술 논리(Description Logic)의 의미 모델에 기반을 두고 있는 OWL언어로 표준화되고 있다. 그러나, 온톨로지 언어를 사용한 직접적인 온톨로지 구축은 거의 불가능하다. 본 논문에서는 지식 표현의 기초가 되고 OWL의 이론적 기반이 되고 있는 프레임 구조로 개념 모델링 하는 방법을 통해 OWL기반의 온톨로지 구축을 보다 편리하고 효과적으로 수행할 수 있는 방법을 제공하며, 효율적인 OWL 문서의 생성과 편집 방안을 도출한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.91-91
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• 2020

IPCC (Intergovernmental Panel on Climate Change) 5차 평가보고서에 의하면 최근 배출 온실가스의 양은 관측 이래 최고 수준이며 온실가스로 인한 기후변화는 인간계와 자연계에 광범위한 영향을 주고 있다고 보고하였다. 기후변화의 영향은 국제적으로 빙하 감소, 사막화, 해수면 상승 등 뚜렷하게 나타나고 있다. 이러한 기후변화에 대응하기 위해 온실가스 완화 정책과 동시에 새로운 기후변화 환경에 적응하는 것이 필요하다. 기후변화 적응이란 현재 나타나고 있거나 미래에 나타날 것으로 예상되는 기후변화의 파급효과와 영향에 대응할 수 있도록 하는 모든 행동이며 이를 위해서는 기후변화 영향분석이 수반되어야 한다. MOTIVE 연구단에서는 기후변화 적응대책 수립의 지원을 목표로 7개 부문(건강, 물관리, 농업, 산림, 생태, 해양, 수산)에서 "한국형 통합평가 모형"을 개발하고 있다. 각 부문에서 개발하는 프로세스 모델은 시스템에 대한 지식을 가진 상황에서 사용하면 신뢰할 수 있는 예측 결과를 얻을 수 있지만, 부문별 통합을 통한 영향 분석 시 타 분야에 대한 지식이 수반되어야 하는 어려움을 가진다. 이를 위해 본 연구에서는 시스템 내의 물리적 프로세스에 대한 요구 없이 입출력 데이터만을 이용하여 결과를 신속하게 추정하는 데이터 모델링(기계학습)을 이용하였다. 데이터 모델링을 위한 데이터는 다양한 자연 현상에 대한 BANPOL(수질 프로세스 모델) 분석을 통한 자료를 이용하여 학습 자료를 구축하였다. 즉, 데이터 모델링은 BANPOL 모델을 대리하는 메타모델이며, 낙동강 표준유역에 대한 유량 및 수질을 높은 상관성으로 추정하였다. 원 모델보다 정확도는 낮을 수 있으나 메타모델의 개발을 통한 웹 시스템을 개발하여 비전문가의 구동 및 신속한 기후 시나리오를 적용할 수 있는 환경을 개발하였다.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.313-323
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• 2015

The purpose of this study is to investigate the cognitive process of student's modeling on a modeling-emphasized argument-based general chemistry experiment. The participants were twenty-one freshman students. Six topics were carried out during the first semester and semi-structured interview was implemented at the end of the semester. Semi-structured interview questions were used to elicit elements of effective model, modeling strategies, difficulties that students have experienced during modeling, and resolving the difficulties that students have experienced during modeling. All student interview data were collected and transcribed. The results of this study are summarized as follows: (1) Elements of effective model were considered to be visual expression, persuasive explanation, and rhetorical structure. (2) Modeling strategies included arranging important keywords or writing the outline, and during the modeling process, students used various data, suggested data after reconstructing, suggested definitions and explanations of core concepts, used meta-cognition, and considering rhetorical structure. (3) Difficulties students have experienced during modeling could be categorized as lack of modeling strategy and understanding. (4) Resolving difficulties students have experienced during modeling could be categorized as modeling strategy and understanding. Students learn the strategy by feedback, modeling experience, evaluation of experimental report, models which they constructed previously and references, and the understanding of contents were achieved through arguments which occurred during classes and during the process of writing the experimental reports. These results suggest that when using modeling in teaching and learning, the argument-based learning strategy can be effective in enhancing students' modeling by helping them to understand meta-modeling with scientific concepts.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Journal of Information Management
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• v.32 no.2
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• pp.40-53
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• 2001

The purpose of this study is to explore the current situation and problems in standardizing knowledge information and information technology in the KISTI, and collect the basic data necessary for establishing the national information infrastructure. Through a survey and interviews with nine departments of the Information Services Sector of the KISTI, data were collected regarding the management and exchange of information and the operation of communication networks. According to the analysis of fifty KISTI databases and their operation, tasks to be first standardized include Classification of ST resources, Metadata, Date modeling, Data format, DBMS, ST terminology, and Retrieval protocol.

• Journal of KIISE
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• v.44 no.11
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• pp.1178-1193
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• 2017

It is very important to understand system behaviors in collective pattern for each knowledge domain. However, there are structural limitations to represent collective behaviors because of the size of system components and the complexity of their interactions, causing the state explosion problem. Further composition with other systems is mostly impractical because of exponential growth of their size and complexity. This paper presents a practical method to model the collective behaviors, based on a new concept of domain engineering: behavior ontology. Firstly, the ontology defines each collective behavior of a system from active ontology. Secondly, the behaviors are formed in a quantifiably abstract lattice, called common regular expression. Thirdly, a lattice can be composed with other lattices based on quantifiably common elements. The method can be one of the most innovative approaches in representing system behaviors in collective pattern, as well as in minimization of system states to reduce system complexity. For implementation, a prototype tool, called PRISM, has been developed on ADOxx Meta-Modelling Platform.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.77-100
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• 2018

The objectives of this study is to present a discovering process of interdisciplinary convergence technology using text mining of big data. For the convergence research of biotechnology(BT) and information communications technology (ICT), the following processes were performed. (1) Collecting sufficient meta data of research articles based on BT terminology list. (2) Generating intellectual structure of emerging technologies by using a Pathfinder network scaling algorithm. (3) Analyzing contents with topic modeling. Next three steps were also used to derive items of BT-ICT convergence technology. (4) Expanding BT terminology list into superior concepts of technology to obtain ICT-related information from BT. (5) Automatically collecting meta data of research articles of two fields by using OpenAPI service. (6) Analyzing contents of BT-ICT topic models. Our study proclaims the following findings. Firstly, terminology list can be an important knowledge base for discovering convergence technologies. Secondly, the analysis of a large quantity of literature requires text mining that facilitates the analysis by reducing the dimension of the data. The methodology we suggest here to process and analyze data is efficient to discover technologies with high possibility of interdisciplinary convergence.