• Journal of KIISE:Computing Practices and Letters
• /
• v.10 no.1
• /
• pp.1-13
• /
• 2004

The Semantic Web introduces the next generation of the Web by establishing a semantic layer of machine-understandable data to enable machines (i.e intelligent agents) retrieve more relevant information and execute automated web services using semantic information. Ontology-related technologies are very important to evolve the World Wide Web of today into the Semantic Web in representation and share of semantic data. In this paper, we proposed and implemented the efficient ontology management system, K-Box, which constructs and manages ontologies using topic maps. We can use K-Box system to construct, store and retrieve ontologies. K-Box system has several components: Topicmap Factory, Topicmap Provider, Topicmap Query Processor, Topicmap Object Wrapper, Topicmap Cache Manager, Topicmap Storage Wrapper.

• Informatization Policy
• /
• v.27 no.4
• /
• pp.24-46
• /
• 2020

This study proposes a way to timely forecast open government data (OGD) demand(i.e., OGD requests, search queries, etc.) by using keyword network analysis. According to the analysis results, most of the OGD belonging to the high-demand topics are provided by the domestic OGD portal(data.go.kr), while the OGD related to users' actual needs predicted through topic association analysis are rarely provided. This is because, when providing(or selecting) OGD, relevance to OGD topics takes precedence over relevance to users' OGD requests. The proposed keyword network analysis framework is expected to contribute to the establishment of OGD policies for public institutions in the future as it can quickly and easily forecast users' demand based on actual OGD requests.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.238-240
• /
• 2004

현재의 웹은 사용자가 원하는 정보를 정확하고 빠르게 검색 결과를 보여주지 못하는 단점이 있다. 그러므로 사용자에게 정확한 정보 전달을 해 주고자 시맨틱 웹이 등장하게 되었다. 시맨틱 웹은 기계가 이해할 수 있는 온톨로지를 구성하여 사용자가 원하는 정보를 정확하게 전달해 줄 수 있다는 점에서 미래의 웹으로 각광을 받게 될 것이다. 시맨틱 웹의 기반이 되고 있는 온톨로지는 어떤 특정 도메인에서 사용되는 정보들과 그 정보들 간의 관계를 정의해 놓은 것으로 관련 도메인 전문가들과 협의에 의하여 개념들과 관계들의 구조를 정하고 이를 기반으로 구축된다. 실제의 응용 시스템에서는 도메인마다의 구체적인 지식을 포함하는 온톨로지 설계가 필요하다. 이 논문에서는 택배회사가 홈쇼핑사이트 업체를 대상으로 효율적인 마케팅을 하기 위친 홈쇼핑사이트에 대한 기본정보를 추출하는 것을 목적으로 한다. 온톨로지를 구축하는 온톨로지 언어에는 RDF, RDF(S), DAML＋OIL, OWL. Topic Map등이 있다. 이 논문에서는 토픽맵을 사용하여 홈쇼핑 사이트 정보를 검색하기 위한 홈쇼핑 사이트에 대한 온톨로지를 설계하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.109-112
• /
• 2004

콘텐츠 관리 시스템(CMS:Contents Management System)은 '자료 수집, 등록, 검색, 배포'의 기본 흐름을 가진다. 콘텐츠의 등록 및 검색/배포를 위하여 각 시스템은 콘텐츠에 대한 별도의 메타 파일들을 가지고 있으나, 이러한 메타파일들은 데이터 요소의 중의성이나 모호함 때문에 일관되고 객관화 된 스키마를 가지지 못하여 체계적 분류 및 최신 업데이트를 위한 메타데이터 자체의 효율적 관리 및 연관 검색 기능을 가지고 있지 않았다. 본 논문에서는 기존 연구되고 있는 MDR과 토픽맵을 자체 개발 중인 콘텐츠 메타파일 관리 시스템(CMMS:Contents Metafile Management System)에 적용하여 메타파일의 체계적이고 효율적인 관리를 통해 기준요소로서의 메타파일 역할을 극대화하고 향후 타 체계와의 연동 및 확장성의 향상을 도모한다.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.1
• /
• pp.100-104
• /
• 2010

In the past decade, various search engines have been developed to retrieve web pages that web surfers want to find from world wide web. In search engines, one of the most important functions is to evaluate and rank web pages for a given web surfer query. The prior algorithms using hyperlink information like PageRank incur the problem of 'topic drift'. To solve the problem, relevance propagation models have been proposed. However, these models suffer from serious performance degradation, and thus cannot be employed in real search engines. In this paper, we propose a new ranking algorithm that alleviates the topic drift problem and also provides efficient performance. Through a variety of experiments, we verify the superiority of the proposed algorithm over prior ones.

• Journal of the Korean Applied Science and Technology
• /
• v.40 no.4
• /
• pp.724-732
• /
• 2023

In this study, we tried to analyze the characteristics of color cosmetics information search and the major information of interest in the color cosmetics market after COVID-19 shown in the text mining analysis results by collecting data on online interest information of consumers in the color cosmetics market after COVID-19. In the empirical analysis, text mining was performed on all documents such as news, blogs, cafes, and web pages, including the word "color cosmetics". As a result of the analysis, online information searches for color cosmetics after COVID-19 were mainly focused on purchase information, information on skin and mask-related makeup methods, and major topics such as interest brands and event information. As a result, post-COVID-19 color cosmetics buyers will become more sensitive to purchase information such as product value, safety, price benefits, and store information through active online information search, so a response strategy is required.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06b
• /
• pp.200-204
• /
• 2008

검색엔진을 사용해 질의를 입력 후 사용자가 원하는 정보를 얻을 때까지의 검색 결과정보의 탐색 범위에 대해 설문한 연구 보고서에 검색 결과정보의 첫 페이지만 보는 사용자가 설문인원의 41%를 차지했고, 상위 3페이지만 사용하는 사용자는 88%에 달한다고 하였다. 따라서 검색결과의 상위순위는 사용자의 정보 존재여부를 판단하는 중요한 척도가 된다. 또한 인터넷의 방대한 정보로 인해 정보 홍수에 빠진 사람들은 정보에 대한 까다로운 요구를 하고 있다. 이를 테면 개인화 또는 맞춤화된 정보를 제공 받기를 원하고 있다. 정보검색시 대다수의 사용자들은 질의의 길이를 2단어 이하의 키워드를 사용하여 질의가 특정한 토픽을 지향하도록 하고 있다. 본 논문에서는 데이터 마이닝의 연관규칙을 적용 사용자 프로파일 DB내 질의에 대한 사용자 질의패턴을 분석하여 '분석 Agent' 통한 연관 질의 리스트를 생성하고 '추천 Agent'는 사용자들의 취향변화 즉 시간에 따라 변하는 관심영역 또는 사용자 질의 변화에 대해서 날짜별 가중치를 부여하여 사용자와 상호교류를 통해 사용자에게 맞춤형 질의를 추천하는 방안을 제시하고자 한다.

• Journal of The Korean Association of Information Education
• /
• v.26 no.5
• /
• pp.467-480
• /
• 2022

This paper aimed to explore research trends for the conceptualization of IT humanities. Reflecting domestic and international references which focused on the possibility of the integration of digital technology and humanities, the authors examined the beginning, background, and relevant concepts of IT humanities to figure out the meaning and the research trends. In addition, using the search word "IT humanities," the authors analyzed network topics of the keywords retrieved from 1,566 KCI and 64 SCI journal articles published since 2001. The concept of IT humanities in the previous studies has tended to associate with competencies that allow considering various fields of IT based on the lens of humanities perspectives. The result of the topic modeling revealed four groups as fields to be integrated with IT humanities, methods of implementation, connections of literature or culture, and creations of IT humanities. Instead of instrumentalization or merger by one stance of IT or humanities, it is imperative to collaboratively work for the generation of a new viewpoint through mutual respect of disciplines.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38B no.5
• /
• pp.385-393
• /
• 2013

In the past decade, a paradigm shift from machine-centered to human-centered and from technology-driven to user-driven has been witnessed. Consequently, Social search is getting more social and Social Network Service (SNS) is a popular Web service to connect and/or find friends, and the tendency of users interests often affects his/her who have similar interests. If we can track users' preferences in certain boundaries in terms of Web search and/or knowledge sharing, we can find more relevant information for users. In this paper, we propose a novel Topic Sensitive_Social Relationship Rank (TS_SRR) algorithm. We propose enhanced Web searching idea by finding similar and credible users in a Social Network incorporating social information in Web search. The Social Relation Rank between users are Social Relation Value, that is, for a different topics, a different subset of the above attributes is used to measure the Social Relation Rank. We observe that a user has a certain common interest with his/her credible friends in a Social Network, then focus on the problem of identifying users who have similar interests and high credibility, and sharing their search experiences. Thus, the proposed algorithm can make social search improve one step forward.

• Journal of KIISE:Software and Applications
• /
• v.31 no.5
• /
• pp.595-604
• /
• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.