• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.117-119
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• 1999

전자상거래에서 판매 실적을 높이기 위한 효과적인 방법의 하나는 사용자에 따라 개별화된 정보의 제공, 즉 원투원 마케팅의 개념을 도입하는 것이다. 이를 위해서는 사용자의 구매 성향이나 사용자의 특성에 대한 지식베이스가 있어야 한다. 이러한 지식베이스로 데이터마이닝 기법중의 하나인 연관규칙을 도입하였다. 본 논문에서는 연관규칙을 기본 연산으로 하는 데이터마이닝 시스템의 설계와 구현을 기술하였다. 사용자와 제품간의 연관규칙을 추출하여 동적으로 제공되는 웹 문서를 생성하는데 필요한 지식베이스를 구축하였다. 또한 구축된 데이터마이닝 시스템은 연관규칙 탐사 엔진과 개념 계층 관리기로 구성되어 있으며, 대용량의 데이터를 다루기 위해 기존의 방법과는 다른 파일을 기반으로 한 빈번항목집합 인덱싱 기법을 제시하였다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.533-538
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• 2023

문서에서 저자의 의도와 주제, 그 안에 포함된 감성을 분석하는 것은 자연어 연구의 핵심적인 주제이다. 이와 유사하게 특정 글에 포함된 정치적 문화적 편향을 분석하는 것 역시 매우 의미 있는 연구주제이다. 우리는 최근 발생한 한 사건에 대하여 여러 신문사와 해당 신문사에서 생산한 기사를 중심으로 해당 글의 정치적 편향을 정량화 하는 방법을 제시한다. 그 방법은 선택된 주제어들의 문장 공간에서의 거리를 중심으로 그래프를 생성하고, 생성된 그래프의 기계학습을 통하여 편향과 특징을 분석하였다. 그리고 그 그래프들의 시간적 변화를 추적하여 특정 신문사에서 특정 사건에 대한 입장이 시간적으로 어떻게 변화하였는지를 동적으로 보여주는 그래프 애니메이션 시스템을 개발하였다. 실험을 위하여 최근 이슈에 대하여 12개의 신문사에서 약 2000여 개의 기사를 수집하였다. 그 결과, 약 82%의 정확도로 일반적으로 알려진 정치적 편향을 예측할 수 있었다. 또한, 학습 데이터에 쓰이지 않은 신문기사를 활용하여도 같은 정도의 정확도를 보임을 알 수 있었다. 우리는 이를 통하여 신문기사에서의 정치적 편향은 작성자나 신문사의 특성이 아니라 주제어들의 문장 공간에서의 거리 관계로 특성화할 수 있음을 보였다. 할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.174-179
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• 2015

Failure Mode and Effects Analysis (FMEA) has been used by Dynamic Positioning (DP) system for risk and reliability analysis. However, there are limitations associated with its implementation in offshore project. 1) since the failure data measured from the SCADA system is missing or unreliable, assessments of Severity, Occurrence, Detection are based on expert's knowledge; 2) it is not easy for experts to precisely evaluate the three risk factors. The risk factors are often expressed in a linguistic way. 3) the relative importance among three risk factors are rarely even considered. To solve these problems and improve the effectiveness of the traditional FMEA, we suggest a Fuzzy-FMEA method for risk and failure mode analysis in Dynamic Positioning System of offshore. The information gathered from DP FMEA report and DP FMEA Proving Trials is expressed using fuzzy linguistic terms. The proposed method is applied to an offshore Dynamic Positioning system, and the results are compared with traditional FMEA.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.189-199
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• 2009

The amount of information on the Web is explosively growing as the Internet gains in popularity. However, only a small portion of the information on the Web is truly relevant or useful to the user. Thus, offering suitable information according to user demand is an important subject in information retrieval. In e-commerce, the recommender system is essential to revitalize commercial transactions, raise user satisfaction and loyalty towards the information provider. The existing recommender systems are mostly based on user data collected at servers, so user data are dispersed over several servers. Therefore, web servers that lack sufficient user behavior data cannot easily infer user preferences. Also, if the user visits the server infrequently, it may be hard to reflect the dynamically changing user's interest. This paper proposes a novel personalization system analyzing the user preference based on web documents that are accessed by the user on a user device. The system also identifies non-content blocks appearing repeatedly in the dynamically generated web documents, and adds weight to the keywords extracted from the hyperlink sentence selected by the user. Therefore, the system establishes at an early stage recommendation strategies for the web server that has little user data. Also, user profiles are generated rapidly and more accurately by identifying the information blocks. In order to evaluate the proposed system, this study collected web data and purchase history from users who have current purchase activity. Then, we computed the similarity between purchase data and the user profile. We confirm the accuracy of the generated user profile since the web page containing the purchased item has higher correlation than other item pages.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.754-759
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• 2007

The information recommendation system offers selected documents according to information needs of dynamic users. User's needs are expressed as profiles consisting of one or more words and may be changed into some specifics through relevance feedback made by users during the recommendation process. In previous research, users have entered relevance information by taking part in explicit relevance feedbacks and learned user profiles using the positive relevance feedbacks. In this paper, we learn user profiles using not only positive relevance feedback but negative relevance feedback and reinforcement learning. To compare the proposed with previous method, we performed experiments to evaluate recommendation performance of the same topic. As a result, the former shows the improved performance than the latter does.

• Journal of KIISE
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• v.41 no.11
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• pp.992-1005
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• 2014

Coreference resolution finds all expressions that refer to the same entity in a document. Coreference resolution is important for information extraction, document classification, document summary, and question answering system. In this paper, we adapt Stanford's Multi-pass sieve system, the one of the best model of rule based coreference resolution to Korean. In this paper, all noun phrases are considered to mentions. Also, unlike Stanford's Multi-pass sieve system, the dependency parse tree is used for mention extraction, a Korean acronym list is built 'dynamically'. In addition, we propose a method that calculates weights by applying transitive properties of centers of the centering theory when refer Korean pronoun. The experiments show that our system obtains MUC 59.0%, $B_3$ 59.5%, Ceafe 63.5%, and CoNLL(Mean) 60.7%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1661-1669
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• 2004

A rapid development of Internet is changing users' desire from existing static contents to dynamic and diverse ones. Thus the SVG provides more affluent and sophisticated graphic expressions than an existing method based on bitmap, it can faithfully display vector graphics without scarifying any functions or the problem of device compatibility. In addition, it allows indexing, searching, storing, and sharing by the description of the logical structure of graphics. Since there are, however, very few people who know the complex SVG syntax and make use it, an editing system, which enables users to utilize the SVG graphics easily, has been in need. Therefore, in this thesis, we do research on basic technology on the SVG, design and make an editing system for SVG documents. The system, therefore, provides uses with a user-friendly editing interface and enables them to write graphic objects easily, and generates complex SVG documents automatically.

• The Journal of the Korea Contents Association
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• v.2 no.2
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• pp.42-46
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• 2002

Transfer of much informations or document necessitated between corporation or corporation and customer by fast growth of electronic commerce. So, it is acting by important success urea that make and manage standard of information in electronic commerce. It was work that work that make standard of information electron catalogue system or EDI environment is very difficult before XML comes out and expense costs much. But, it became easy that make standard of information by XML's entrance on the stage that have advantages of SGML and HTML Specially, it is very suitable that electronic commerce inflects XML as field that heterogeneous system exists and standardization is required urgently. That is, XML is big advantage in side that make out side that is and normalized document because store information in standard form that is applied well under heterogeneous system environment because do web environment by basis interface and can be used efficiently to transmit. Treatise that see hereupon could assume beforehand in browser control using XSLT. And using dynamic DLL XML catalogue edit system design and implementation.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.521-526
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• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1072-1079
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• 2003

This study is purported for suggesting a new clustering algorithm that enables incremental categorization of numerous documents. The suggested algorithm adopts the natures of the spherical k-means algorithm, which clusters a mass amount of high-dimensional documents, and the fuzzy ART(adaptive resonance theory) neural network, which performs clustering incrementally. In short, the suggested algorithm is a combination of the spherical k-means vector space model and concept vector and fuzzy ART vigilance parameter. The new algorithm not only supports incremental clustering and automatically sets the appropriate number of clusters, but also solves the current problems of overfitting caused by outlier and noise. Additionally, concerning the objective function value, which measures the cluster's coherence that is used to evaluate the quality of produced clusters, tests on the CLASSIC3 data set showed that the newly suggested algorithm works better than the spherical k-means by 8.04% in average.