• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.348-349
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• 2017

본 논문에서 제안하는 내용은 기존의 포털 검색의 키워드 입력 방식과는 달리, 검색하고자 하는 대상을 스마트폰과 같은 모바일 기기의 카메라로 촬영하면, 해당 촬영 이미지가 사용자 입장에서는 검색 키워드와 같이 동일한 역할을 할 수 있도록 이미지에 해당되는 검색 키워드를 추출 및 매칭하여 검색을 위한 질의어로 사용할 수 있도록 해주는 것을 목적으로 한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.195-197
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• 1999

본 연구에서는 인터넷 상의 많은 문서들 중에서 사용자에게 보다 적합한 문서를 제공하기 위해 퍼지 관계성을 이용하여 검색 결과 집합의 문서에서 추출한 키워드간의 유사클래스를 생성한다. 또한, 기존의 키워드 직접 매칭에 의한 검색 방법의 단점이라 할 수 있는 의미적 관계를 가지는 문서에 대한 검색 방법도 제안한다. 생성된 유사 클래스는 사용자의 질의를 확장하여 사용자의 관심도를 보다 많이 반영하게 되고, 그 질의어가 포함된 단어나 구의 발생 빈도수가 높은 문서에 대해 의미적으로 서로 연결시켜 분류한다. 본 연구에서 제안한 알고리즘에 의해 문서를 사용자 관심 정도로 분류, 카테고리를 생성하여 검색 효율을 증대시키고 사용자의 요구에 적합한 결과를 제공하고자 한다.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.237-243
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• 2016

Since realtime search keywords of portal sites are arranged in descending order by instant increasing rates of search numbers, they easily show issues increasing in interests for a short time. But they have the limits extracted different results by portal sites and not shown issues by a period. Thus, to find key issues from the whole realtime search keywords for certain period, and to show results of summarizing them and analyzing differences, is significant in providing the basis of understanding issues more practically and in maintaining consistency of them. This paper analyzes differences of week key issues extracted from week analysis of realtime search keywords provided by two typical portal sites. The results of experiments show that the portal group means of realtime search keywords by the independent t-test and the survival functions of realtime search keywords by the survival analysis are statistically significant differences.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1265-1267
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• 2022

방대한 분량의 콘텐츠 홍수 속에서 원하는 소재를 찾기 위해 콘텐츠 내용을 검색할 수 있는 효과적인 방법이 지원되는 것은 창작을 자유롭게 하고, 콘텐츠 활용도를 높이기 위해 매우 중요하다. KBS 바다 서비스의 경우 분류체계 방법을 사용하고 있으나. 최근 딥러닝을 이용한 인공지능 기술의 발전으로 콘텐츠의 내용을 인공지능 기술로 태깅하고, 태깅된 텍스트 정보를 이용하여 검색할 수 있는 기술 개발이 활발히 수행되고, 국가적으로도 해당 기술을 지원하고 있다. 본 논문에서는 이러한 기술 개발의 선행 요소인 방송사의 제작과정에서 요구되는 동영상 소재 콘텐츠 검색의 요구사항을 KBS 비디오 아카이브 검색 키워드 실제 사용 데이터를 이용하여 분석하였다. 약 1,000여건의 검색 키워드 분석과 이용자와 운영자의 응답 내용을 고찰한 결과, 특정 키워드에 집중하여 검색할 수 있도록 보완하여 주는 것이 필요함을 알아내었다. 또한, 검색 범위를 효과적으로 축소하여 검색을 손쉽고 빠르게 할 수 있는 방법을 고찰하였다. 본 논문에서는 미디어 창작에서 필요한 소재 콘텐츠를 찾기 위해 연구 개발해야 할 미디어 속성 추출 기술의 방향성을 제시하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.421-429
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• 2006

Recently, DHT-based Structured P2P System have a attention to scalability and providing efficient lookup by routing. Retrieving content of DHT-based P2P require knowledge of the exact identifier of sharing file. But user may wish to search for sharing file using descriptive keyword or content. To resolve the problem, this paper propose Extended-CAN mechanism to support keyword search. We defined content-based keyword and identifier of sharing file, and PLS extended to KID and CKD for keyword, common keyword processing. As a result, Extended-CAN mechanism provide efficient keyword search for DHT-based Structured P2P System.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1063-1066
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• 2005

Most reading systems that supply various study data generally support keyword search. But the usual keyword matching techniques have a problem to require the exact keyword matching, and could not find similar field materials. Futhermore, testing materials have too little information to apply the keyword matching search. To solve these problems, this thesis proposes the method to extract the important keyword from study data and to construct the database automatically when the data are stored at the storage. And using prepared similar terminology database, we suggest the intelligent and efficient technique to find study materials.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2185-2196
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• 1997

The existing information retrieval systems utilize thesaurus in order to search and retrieve the desired information even when the query is not accurate. However the cost for implementing and maintaining thesaurus is very high and it can not guarantee complete success of search/retrieval operation. Thus in this paper, Information Retrieval System using Keyword Network(KNetIRS) which was designed and implemented to solve these problem is introduced. Keyword Network composed of keywords which were extracted from documents. KNetIRS finds the appropriate documents by using the Keyword Network which is based on the concept of "inverted file". In addition, KNetIRS can carry out query expansion by using the Keyword Network Browser, and deal with the conjunction of "정보 검색", "정보", and "검색", by defining and implementing spilt function.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1832-1840
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• 1999

Since the existed Korean Web IR systems generally use boolean system, it is difficult to retrieve the information to be wanted at one time. Also, because of the feature that web documents have the frequent abbreviation and many links, the keyword extraction using the inverted document frequency extracts the improper keywords for adding ambiguous meaning problem. Therefore, users must repeat the modification of the queries until they get the proper information. In this paper, we design and implement the relevance feedback agent system for resolving the above problems. The relevance feedback agent system extracts the proper information in response to user's preferred keywords and stores these keywords in preference DB table. When users retrieve this information later, the relevance feedback agent system will search it adding relevant keywords to user's queries. As a result of this method, the system can reduce the number of modification of user's queries and improve the efficiency of the IR system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2485-2487
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• 2015

Recently, Hashtag is widely used in SNS like Facebook, Twitter and personal blogs. However, the efficiency of tag search system is poor due to the indiscriminate use of hashtags. To enhance the accuracy of tag search system, we proposed a tag search system using the keyword extraction and similarity evaluation. The experimental results show that the proposed system provides the higher accuracy on tag search results.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.51-56
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• 2009

In a language, noun and keyword extraction is a key element in information processing. When it comes to processing Korean language information, however, there are still a lot of problems with noun and keyword extraction. This paper proposes an effective noun extraction method that considers noun emergence features. The proposed method can be effectively used in areas like information retrieval where large volumes of documents and data need to be processed in a fast manner. In this paper, a category-based keyword construction method is also presented that uses an unsupervised learning technique to ensure high volumes of queries are automatically classified. Our experimental results show that the proposed method outperformed both the supervised learning-based X2 method known to excel in keyword extraction and the DF method, in terms o classification precision.