• Journal of Information Processing Systems
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• v.10 no.2
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.520-522
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• 2008

공공기관이 고객을 위한 품질행정(품질경영)의 구현이라는 사명을 달성하는데 있어서 ISO 시스템이 큰 기여를 할 수 있지만 민간기업에 사용되는 ISO 시스템을 그대로 공공부문에 적용할 경우, 많은 무리와 저항이 따르게 되었고 이러한 문제를 극복하기 위해 KISTI는 다각적으로 노력하였다. 이에 KISTI는 2003년 국내 최초로 정보유통 분야에서 ISO 체제를 도입하여 콘텐트 유통(수집-분석-가공-입력-구축-서비스) 업무에 표준화된 포맷으로 업무 프로세스 표준화를 통하여 서비스 품질향상 및 고객만족을 위한 지속적인 개선기반을 구축하였다.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.341-360
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• 2015

This paper proposes a noble service architecture based on scientific infographic as well as semi-automatic knowledge process for 'KISTI's Scent of Science' database, which has been highly credited as a science journalism service in Korea. Unlike other specialized scientific databases for domain experts and scientists, the database aims at providing comprehensible and intuitive information about various important scientific concepts which may seem not to be easily understandable to general public. In order to construct a knowledge-base from the database, we deeply analyze the traits of the database and then establish a semi-automatic approach to identify and extract various scientific intelligence from its contents. Furthermore, this paper defines a scientific infographic service platform based on the knowledge-base by offering its detailed structure, methods and characteristics, which shows a progressive future direction for science journalism service.

• Journal of the Korean Institute of Gas
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• v.25 no.3
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• pp.9-15
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• 2021

In this paper, a study on the construction of a knowledge base based on natural language processing and the design of a chemical substance estimation system for the development of a knowledge service for a real-time sensor information fusion detection system and symptoms of contact with chemical substances in industrial sites. The information on 499 chemical substances contact symptoms from the Wireless Information System for Emergency Responders(WISER) program provided by the National Institutes of Health(NIH) in the United States was used as a reference. AllegroGraph 7.0.1 was used, input triples are Cas No., Synonyms, Symptom, SMILES, InChl, and Formula. As a result of establishing the knowledge base, it was confirmed that 39 symptoms based on ammonia (CAS No: 7664-41-7) were the same as those of the WISER program. Through this, a method of establishing was proposed knowledge base for the symptom extraction process of the chemical substance estimation system.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• International Journal of Knowledge Content Development & Technology
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• v.2 no.2
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• pp.17-29
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• 2012

All academic information on the web or elsewhere has its creator, that is, a subject who has created the information. The subject can be an individual, a group, or an institution, and can be a nation depending on the nature of the relevant information. Most information is composed of a title, an author, and contents. An essay which is under the academic information category has metadata including a title, an author, keyword, abstract, data about publication, place of publication, ISSN, and the like. A patent has metadata including the title, an applicant, an inventor, an attorney, IPC, number of application, and claims of the invention. Most web-based academic information services enable users to search the information by processing the meta-information. An important element is to search information by using the author field which corresponds to a personal name. This study suggests a method of efficient indexing and using the adjacent operation result ranking algorithm to which phrase search-based boosting elements are applied, and thus improving the accuracy of the search results of personal names. It also describes a method for providing the results of searching co-authors and related researchers in searching personal names. This method can be effectively applied to providing accurate and additional search results in the academic information services.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• Annual Conference on Human and Language Technology
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• 1999.10d
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• pp.3-5
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• 1999

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.102-104
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• 1998

본 시스템은 현재 연구 개발중인 정보에이전트 시스템의 일부로서 특정분야에 대한 사용자의 관심 주제에 관련된 정보와 함께 적절한 문서를 제공하는 지식 기반 시스템이다. 이러한 목적을 위해서 본 시스템의 지식베이스는 구조적인 방식으로 표현된 BKB(Biology Knowledge Base)와 DIC(DICtionary)로 구성된다. DIC는 특정분야에서 일반적으로 사람들이 사용하는 용어와 학명을 기준으로 하는 시스템에서 사용하는 용어와의 관계와 그러한 용어들간의 동의어 관계를 갖고 있다. 또한 BKB는 동물에 관련된 지식베이스로써 상위.하위 개념과 함께 사용자가 원하는 정보를 제공하기 위해 객체의 속성과 이에 관계된 값들을 포함한다. 본 시스템은 문서를 검색할 때 사용자 초기 질의를 상위.하위 개념 그리고 동의어로 확장할 뿐만 아니라 사용자 의도의 정확한 표현을 위해서 제공하는 다양한 질의 형식에 따른 질의 처리 결과로도 확장하므로 효과적인 문서 검색 결과를 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.385-389
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• 2007

전통적인 디지털 박물관 시스템은 전시를 조직화하는 단순한 기능에 촛점을 두고 있으며 설계 패턴은 전시 항목들에 기반을 두고 있다. 이것의 생명주기는 직선적이고 피드백과 재사용성이 부족하여 생명 주기에서 지식 처리를 다루는 것에 대해서는 관심을 가지지 않고 있다. 하지만 현대 디지털 박물관은 급진적으로 증가하는 정보를 다루어야 하고, 디지털 감상, E-학습과 관련 연구를 위해 통합된 기능을 제공해야 한다. 이러한 요구사항들은 디지털 박물관에서 객체들이 고수준으로 추상화되고, 생명주기도 반복적이며 재사용이 가능하도록 요구하고 있다. 그러므로 체계적으로 통합된 지식처리 과정들이 디지털 박물관에서 정보시스템을 다루기 위해서 절대적으로 필요하게 되었다. 본 논문에서는 현대 디지털 박물관 시스템에서 새로운 생명주기를 제안한다. 지식 흐름(knowledge flow)은 디지털 박물관 생명주기에 걸쳐서 모든 정보 흐름으로부터 고수준으로 추상화된 객체이다. 지식 흐름을 따라가게 되면 이 특별한 생명주기는 정의된 시금석으로서 다차원의 분수 모델(Fountain Model)과 비슷하며, 이 생명주기에서 지식처리 과정은 각 차원에서 다른 강조점을 지닌 계층으로 잘 나누어진다. 또한 분산된 지식 처리 절차를 체계적이고 재사용할 수 있도록 통합하기 위한 지식 기반 소프트웨어 공학 접근방법을 제공한다.