• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10c
• /
• pp.51-55
• /
• 2006

시맨틱 웹은 현재 웹의 확장된 개념으로 사람뿐만 아니라 컴퓨터 스스로가 데이터를 이해하고 처리할 수 있도록 정보에 의미를 부여하는 것이다. 시맨틱 웹 데이터를 기술하는 RDF를 통해 메타데이터를 표현하고 의미론적 추론이 가능하게 되었다. 따라서 기존에 일반 사용자가 쉽게 사용할 수 있는 키워드 검색 방법을 시맨틱 웹 데이터인 RDF/RDF 스키마에 적용함으로써 차세대 웹으로 인식되고 있는 시맨틱 웹을 일반 사용자도 쉽게 활용할 수 있도록 한다. 본 논문에서는 RDF 문서의 효율적인 검색을 위해 RDF 인스턴스와 RDF 스키마 정보를 저장하고, 키워드, 속성, 클래스 타입의 복합 조건 검색을 만족시키는 키워드 인덱스와 스키마 테이블 구조를 제안한다. 본 논문에서 제안한 구조는 다양한 조건들을 만족하는 리소스 정보의 빠르고 정확한 검색이 가능하도록 한다.

• Journal of the Korean Society for information Management
• /
• v.12 no.2
• /
• pp.171-192
• /
• 1995

In this study, we constructed a hypertext-based bibliographic information retrieval system, which is very usell tool to browse and retrieve structured data. We minimized the problem of the use; s disorientation with the keyword retrieval technique. We also presented the potential advantages of this system which could be obtained by implementing it on WWW.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.6
• /
• pp.1403-1408
• /
• 2010

Korean information retrieval uses noun as index terms or keywords of representing the document. and noun and keyword extraction is to find all nouns presented in the document, In this paper, we proposes the method of keyword extraction using pre-built dictionary. This method reduces the execution time by reducing unnecessary operations. And noun, even large documents without affecting significantly the accuracy, can be extracted. This paper proposed noun extraction method using the appearance characteristics of the noun and keyword extraction method using unsupervised learning techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.6
• /
• pp.63-71
• /
• 2011

The current Web portal sites provide significant keywords with high popularity or importance; specifically, user-friendly services such as tag clouds and associated word search are provided. However, in general, since news articles are classified only with their date and categories, it is not easy for users to find other articles related to some articles while reading news articles classified with categories. And the conventional associated keyword service has not satisfied users sufficiently because it depends only upon user queries. This paper proposes a way of searching news articles by utilizing the keywords tightly associated with users' queries. Basically, the proposed method discovers a set of keyword association patterns by using the association rule mining technique that extracts association patterns for keywords by focusing upon sentences containing some keywords. The method enables users to navigate the space of associated keywords hidden in large news articles.

• Journal of Internet Computing and Services
• /
• v.16 no.3
• /
• pp.43-57
• /
• 2015

In this thesis was conducted to propose a method for efficient information retrieval using concept lattices. Since this thesis designed a new system based on ordinary concept lattices, it has the same approach method as ontology, but this thesis proposes new concept lattices to be used by establishing collaborative relations between objects and concepts that users are likely to search information more efficiently. The system suggested by this thesis can be summarized as below. Firstly, this system leads to a collaborative search by using Three kinds of concepts, such as keyword concept lattices, which focus on input key words, expert concept lattices recommended by experts and theme concept lattices, and based on these 3 concept lattices, it will help users search information they want more efficiently. Besides, as the expert concept and the keyword concept become combined, further providing users with the frequency of keyword and the frequency of category, this system can function to recommend key words related to search words entered by users. Another function of this system is to inform users of key words and categories used in users' interested themes by using the theme concept lattices. Secondly, when there is not keyword entered by a user, it is possible for users to achieve the goal of search through the secondary search when this system provides them with key words related to the input keyword. Thirdly, since most of the information is managed while being dispersed, such dispersed and managed information not only has different expression methods but changes as time goes. Accordingly, By using XMDR for efficient data access and integration of distributed information, this thesis proposes a new technique and retrieval system to integrate dispersed data.

• Journal of KIISE
• /
• v.42 no.3
• /
• pp.390-398
• /
• 2015

Spatial web objects refer to web documents that contain geographic information. Recently, services that create spatial web objects have increased greatly because of the advancements in devices such as smartphones. For services such as Twitter or Facebook, simple texts posted by users is stored along with information about the post's location. To search for such spatial web objects, a method that uses spatial information and text information simultaneously is required. Conventional spatial web object search methods mostly use R-tree and inverted file methods. However, these methods have a disadvantage of requiring a large volume of space when building indices. Furthermore, such methods are efficient for searching with many keywords but are inefficient for searching with a few keywords.. In this paper, we propose a spatial web object search method that uses a quad-tree and a patricia-trie. We show that the proposed technique is more effective than existing ones in searching with a small number of keywords. Furthermore, we show through an experiment that the space required by the proposed technique is much smaller than that required by existing ones.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.1710-1712
• /
• 2012

최근 Twitter를 비롯한 소셜 네트워크 서비스의 급속한 확산으로 인해, 많은 수의 SNS 메시지가 실시간으로 생성되고 있다. 이러한 SNS상에서의 단문 글들을 실시간으로 분석하여 최신의 트렌드를 추출해 낼 수 있다면, 사용자에게 유용한 정보를 제공하는 것이 가능하다. 본 논문에서는 다량의 Tweet글들에 대한 실시간 분석을 바탕으로 트렌드를 추출하고 연관된 키워드를 제공하는 기법을 제안한다. 제안하는 기법은 실시간으로 생성되는 Tweet내에서 영어의 언어적 특성을 활용하여 최근 이슈화된 트렌드 키워드를 추출해낸다. 또한, Tweet 내에서 각 트렌드 키워드간 관계를 분석하여 연관 키워드를 제공하며, 동시에 Wikipedia와 Google에서의 검색을 통하여 다른 형태의 연관 키워드도 추출한다. 이 모든 과정은 제안된 트렌드 추출 알고리즘을 통해 실시간으로 제공된다. 제안된 기법을 바탕으로 시스템을 구현하고 다양한 실험을 통하여 키워드의 유효성 및 처리 속도 면에서 시스템의 성능을 평가한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.701-704
• /
• 2010

본 논문에서는 블로그의 포스트로부터 주요 키워드를 추출하여 노래 가사 데이터와 유사도를 분석, 해당 블로그 포스트에 적합한 음악을 추천하는 기법을 제안한다. 또한, 블로거가 포스트마다 제시한 태그들도 주요한 키워드로서 활용한다. 이를 위해서, 첫째로 TF-IDF 기법을 사용하여 텍스트로 구성된 포스트의 중요 키워드를 추출한다. 둘째로 포스트의 태그와 추출된 키워드를 기반으로 유사한 노래 가사를 LSA 기법으로 검색하여 가장 높은 유사도를 갖는 음악을 선택, 적합한 음악으로써 추천한다. 사용자 만족도 평가 실험을 통해서 제안하는 기법이 실제 추천에 적합한지 검증한다.

• Journal of Digital Convergence
• /
• v.11 no.3
• /
• pp.285-292
• /
• 2013

The method of recent information retrieval passes into an semantic search to provide more accurate results than keyword-based search. But in common user case, they are still accustomed to using existing keyword-based search. Hence they are hard to create a typed structured query language. In this paper, we propose to ontology knowledge-base scheme for query interpretation of these user. The proposed scheme was designed based on the OWL-DL for description logic reasoning, it can provide a richer representation of the relationship between the object by using SWRL(Semantic Web Rule Language). Finally, we are describe the experimental results of the similarity measurement for verification of a user query semantic interpretation.

• Journal of KIISE:Databases
• /
• v.33 no.5
• /
• pp.538-550
• /
• 2006

XML Keyword search enables us to get information easily without knowledge of structure of documents and returns specific and useful partial document results instead of whole documents. Element level query processing makes it possible, but computational complexity, as the number of documents grows, increases significantly overhead costs. In this paper, we present document-level ranking scheme over XML documents which predicts results of element-level processing to reduce processing cost. To do this, we propose the notion of 'keyword proximity' - the correlation of keywords in a document that affects the results of element-level query processing using path information of occurrence nodes and their resemblances - for document ranking process. In benefit of document-centric view, it is possible to reduce processing time using ranked document list or filtering of low scored documents. Our experimental evaluation shows that document-level processing technique using ranked document list is effective and improves performance by the early termination for top-k query.