• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.8 no.1
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• pp.49-55
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• 2005

Purpose: The purpose of this study was to evaluate the quality and problems of Web sites for management of childhood and adolescent obesity. Methods: We evaluated 203 Web sites identified from the search engine, Korean Yahoo, using the word of 'childhood and adolescent obesity'. 203 Web sites were classified according to medical institutions, health information Web sites, beauty shops. etc. We surveyed whether childhood and adolescent obesity distinguished with adult obesity was considered, or not. and researched the unique managements of childhood and adolescent obesity including the cardinal treatment. Results: Of the 203 Web sites, 157(77.3%) provided detailed information about treatment of obesity, 46(22.7%) provided only simple information about one. The sites providing detailed information were composed of 52.2% of oriental medicine clinics, 35.0% of clinic & hospitals including pediatric hospitals. Distribution of the sites about management of childhood and adolescent obesity distinguished with adult's one was only 23% of oriental medicine clinics, but 93% of childrens hospitals. Conclusion: Without considering the speciality of childhood obesity, inaccurate information are distributing on internet web sites. It is necessary for concern and development of advertizing system on the internet distributing accurate information about treatment of childhood obesity.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• Korean Journal of Cognitive Science
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• v.15 no.2
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• pp.23-31
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• 2004

Latent Semantic Analysis (LSA Landauer ＆ Dumais, 1997) is a technique to represent the meanings of words using co-occurrence information of words appearing in he same context, which is usually a sentence or a document. In LSA, a word is represented as a point in multidimensional space where each axis represents a context, and a word's meaning is determined by its frequency in each context. The space is reduced by singular value decomposition (SVD). The present study elaborates upon LSA for use of representation of ambiguous words. The proposed LSA applies rotation of axes in the document space which makes possible to interpret the meaning of un. A simulation study was conducted to illustrate the performance of LSA in representation of ambiguous words. In the simulation, first, the texts which contain an ambiguous word were extracted and LSA with rotation was performed. By comparing loading matrix, we categorized the texts according to meanings. The first meaning of an ambiguous wold was represented by LSA with the matrix excluding the vectors for the other meaning. The other meanings were also represented in the same way. The simulation showed that this way of representation of an ambiguous word can identify the meanings of the word. This result suggest that LSA with axis rotation can be applied to representation of ambiguous words. We discussed that the use of rotation makes it possible to represent multiple meanings of ambiguous words, and this technique can be applied in the area of web searching.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.417-426
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• 2000

정보 시스템 개발은 크게 계획, 분석, 설계, 구축의 네 단계로 이루어진다. 이중 사용자 요구사항을 파악하는 분석 단계는 시스템개발 수명주기에 있어 가장 큰 비중을 갖는다. 또한 수명주기의 초기 단계에서 발견되지 못한 결점은 개발이 진행될수록 수정하는데 많은 비용과 노력을 필요로 하게 되어 분석 결과물의 품질은 전체 시스템 품질에 큰 영향을 미치게 된다. 분석 단계의 주요 작업은 데이터 모델링과 프로세스 모델링이다. 이중 데이터 모델리을 위한 지식베이스 시스템 개발에 대한 노력은 기존 연구에서 수행되어 왔으나 프로세스 모델링을 위한 지식베이스 시스템에 대한 연구는 부족하다. 특히 최근 User Interaction이 많은 시스템이 점점 증가하고 있는 추세에 적합한 프로세스 모델링 방법과 지식베이스에 대한 연구가 필요하다.이 연구에서는 사용자 상호작용이 많은 시스템 환경에서의 프로세스 모델링을 위한 절차를 제안하고, 제안된 절차를 효과적으로 지원하고 결과물의 품질을 보증하기 위한 지식베이스 시스템을 구축한다. 모델은 다음의 주요 작업들로 구성된다: 이벤트 분석, 프로세스 분석, 이벤트/프로세스 상호작용 분석. 이벤트 분석은 영향을 주는 이벤트와 그로 인해 수행되어야 하는 업무 절차(Response)를 파악한다. 프로세스 분석은 이벤트 분석과는 독립적으로 수행되며 상위 수준의 업무부터 최하위 수준의 프로세스까지 도출한다. 이벤트/프로세스 상호작용 분석은 이벤트와 프로세스의 분석 결과를 상호 검증하기 위하여 실시된다. 제안된 프로세스 모델링 방법을 지원하기 위한 지식베이스 시스템을 웹 환경에서 구현하였다. 구현된 지능형 robot과 spider 등으로 구성된, 신뢰성 있고 지능적인 MP3 검색 엔진 지원 시스템의 설계와 구현 결과 그리고 성능 등을 종합적으로 요약한다.실어증 환자들은 화시적 대명사를 조응적 대명사보다 더 잘 처리하는 동일한 결과를 보였다. 이러한 실험 결과들은 실어증 환자들이 뇌손상으로 인해 문법적 언어처리에는 어려움을 보이지만 비언어적인, 세상 지식과 관련된 화시적 대명사의 처리는 가능할 것이라는 가설을 뒷받침 해준다. 또한 이러한 실험 결과를 통해 대명사의 기능적인 측면에서 화시와 조응의 처리가 구분되어 있음을 보여준다.l mechanism is concentrate on only the reaction zone. As strain rate and CO2 quantity increase, NO production is remarkably augmented.our 10%를 대용한 것이 무첨가한 것보다 많이 단단해졌음을 알 수 있었다. 혼합중의 반죽의 조사형 전자현미경 관찰로 amarans flour로 대체한 gluten이 단단해졌음을 알수 있었다. 유화제 stearly 칼슘, 혹은 hemicellulase를 amarans 10% 대체한 밀가루에 첨가하면 확연히 비용적을 증대시킬 수 있다는 사실을 알 수 있었다. quinoa는 명아주과 Chenopodium에 속하고 페루, 볼리비아 등의 고산지에서 재배 되어지는 것을 시료로 사용하였다. quinoa 분말은 중량의 5-20%을 quinoa를 대체하고 더욱이 분말중량에 대하여 0-200ppm의 lipase를 lipid(밀가루의 2-3배)에 대

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.493-506
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• 2018

This paper proposes the OOPP(Optimized Online Portfolio Platform) design for the job seekers to search for the job competency necessary for employment and to write and manage portfolio online efficiently. The OOPP consists of three modules. First, JDCM(Job Data Collection Module) stores the help-wanted advertisements of job information sites in a spreadsheet. Second, CSM(Competency Statistical Model) classifies core competencies for each job by text-mining the collected help-wanted ads. Third, OBBM(Optimize Browser Behavior Module) makes users to look up data rapidly by improving the processing speed of a browser. In addition, The OBBM consists of the PSES(Parallel Search Engine Sub-Module) optimizing the computation of a Search Engine and the OILS(Optimized Image Loading Sub-Module) optimizing the loading of image text, etc. The performance analysis of the CSM shows that there is little difference in accuracy between the CSM and the actual advertisement because its data accuracy is 99.4~100%. If Browser optimization is done by using the OBBM, working time is reduced by about 68.37%. Therefore, the OOPP makes users look up the analyzed result in the web page rapidly by analyzing the help-wanted ads. of job information sites accurately.