• Journal of Korean Library and Information Science Society
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• v.40 no.4
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• pp.139-158
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• 2009

This paper used the network analysis method to analyse a variety of attributes of searcher's search behaviors which was appeared on search access log data. The results of this research are as follows. First, the structure of network represented depending on the similarity of the query that user had inputed. Second, we can find out the particular searchers who occupied in the central position in the network. Third, it showed that some query were shared with ego-searcher and alter searchers. Fourth, the total number of searchers can be divided into some sub-groups through the clustering analysis. The study reveals a new recommendation algorithm of associated searchers and search query through the social network analysis, and it will be capable of utilization.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.67-79
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• 2007

As the Web technologies and wired/wireless network are improved and various new multimedia services are introduced recently, need for searching multimedia including video data has been much increasing, The previous approaches for multimedia retrieval, however, do not make use of the relationships among semantic concepts contained in multimedia contents in an efficient way and provide only restricted search results, This paper proposes a multimedia retrieval system exploiting semantic relevancy of multimedia contents based on a domain ontology, We show the effectiveness of the proposed system by experiments on a prototype system we have developed. The proposed multimedia retrieval system can extend a given search keyword based on the relationships among the semantic concepts in the ontology and can find a wide range of multimedia contents having semantic relevancy to the input keyword. It also presents the results categorized by the semantic meaning and relevancy to the keyword derived from the ontology. Independency of domain ontology with respect to metadata on the multimedia contents is preserved in the proposed system architecture.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1491-1499
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• 2011

Now a days, to make good use of tags is a general tendency when users need to upload or search some multimedia data such as images and videos on the Web. In this paper, we introduce an approach to calculate semantic importance of tags and to make re-ranking with them on tagged Web image retrieval. Generally, most photo images stored on the Web have lots of tags added with user's subjective judgements not by the importance of them. So they become the cause of precision rate decrease with simple matching of tags to a given query. Therefore, if we can select semantically important tags and employ them on the image search, the retrieval result would be enhanced. In this paper, we propose a method to make image retrieval re-ranking with the key tags which share more semantic information with a query or other tags based on Wikipedia-based semantic relatedness. With the semantic relatedness calculated by using huge on-line encyclopedia, Wikipedia, we found the superiority of our method in precision and recall rate as experimental results.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.533-543
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• 2012

We in this paper propose a semantic search system based on Korean medicine ontology. Semantic search augments search results and improves search accuracy by understanding which concept denotes terms which users is trying to find. Our semantic search system also provides these semantic search capabilities. Moreover, search scenarios which is meaningful in Korean medicine are designed and implemented by analyzing the semantics of Korean medicine ontology. Therefore, our system can help users find the useful search results with respect to Korean medicine by providing the more meaningful information as well as the connected information in ontology.

• The Journal of Society for e-Business Studies
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• v.16 no.2
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• pp.143-158
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• 2011

This study suggests a framework which extracts features of collective intelligence from social Q&A community sites and takes advantage of those features upon vertical search for domain specific knowledge or information retrieval. One source of collective intelligence on the internet is the question and answer(Q&A) data available from many Q&A sites. Vertical search is focused on searching special areas or specific domains. This paper proposes a framework for extending the relevant terms by using Q&A information connected with query that the user wants to retrieve, and then applies them to specific domain field that requires professional and detailed knowledge.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.285-288
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• 2011

시맨틱 웹상에서 메타 데이터를 표현하는 RDF 데이터에 대한 질의 처리를 위해 여러 가지 RDF 질의어가 제안되었으나 리소스간의 복잡한 관계성들의 발견(discovery)을 위한 충분한 지원을 하지 못하고 있다. 본 논문에서는 시맨틱 연관성 검색 유형의 하나인 ${\rho}$-intersect 연산의 처리 방법을 소개한다. 이를 위해 접미사 배열을 이용한 인덱싱과 ${\rho}$-intersect 연산의 특징을 고려한 최적화 방법을 활용한다. 제안된 처리 기법을 통해 전형적인 RDF 질의 유형뿐만 아니라 시맨틱 연관성 질의 유형도 지원할 수 있도록 한다.

• Journal of the Korean Society for information Management
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• v.10 no.2
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• pp.3-22
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• 1993

In recent years various document ranking methods such as Relevance. R-Distance and K-Distance have been developed wh~ch can be used in thesaurus-based boolean retrieval systems. They give high quality document rankings in many cases by using term dependence lnformatlon from a thesaurus. However, they suffer from several problems resulting from inefficient and Ineffective evaluation of boolean operators AND. OR and NOT. In this paper we propose new thesaurus-based document ranking methods called KB-FSM and KB-EBM by exploitmg the enhanced fuzzy set model and the extended boolean model. The proposed methods overcome the problems of the previous methods and use term dependencies from a thesaurs effectively. We also show through performance comparison that KB-FSM and KBEBM provide higher retrieval effectiveness than Relevance. R-D~stance and K-Distance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.163-164
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• 2022

본 연구는 최근 2년(2019.12.1.~2021. 11.30)간 빅카인즈를 이용하여 '메타버스 AND 비대면 교육' 키워드가 포함된 뉴스 검색 결과 1148건을 바탕으로 관계도 분석, 연관어 키워드 빈도수 및 연관어 가중치 분석을 하였다. 첫째, 관계도 분석에서 가중치 '5'로 적용한 12개의 키워드 가중치로 코로나19(64), 아바타(43), 코로나(22), 유니버스(21), 게더타운(15), 패러다임(12), 신입사원(12), 로블록스(7)로 나타났다. 둘째, 연관어 키워드 월간 빈도수로는 2019.12~ 2020.9(0건), 2020.10(1건), 2021.3(19건), 2021.4(34건), 2021.6(72건), 2021.9 (196건), 2021.11애는 233건으로 급격하게 증가하였다. 셋째 키워드와의 연관성(가중치/키워드 빈도수)으로 코로나19(113.96/515), 가상세계(67.75/ 344), 메타버스(58.36/103), 메타(49.8/5730), 가상공간(45.57/380) 순이었다. 이 분석 결과에서 위드코로나 시대의 비대면 교육으로 메타버스에 기반을 둔 가상공간 활용 교육은 더욱 증가될 것으로 예상된다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3855-3865
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• 2000

The automatic indexing is a compulsory part for the text retrieval system. However it is impossible to rank the appropriate texts at top. Furthermore, it is more difficult to prevent to rank the inappropriate texts having homonyms at top by only the automatic indexing. In this paper, we proposed the two-level retrieval system to enhance the retrieval efficiency, in which Automatic Keyword Network (AKN) is used at the second-level process. The firsHevel search is carried out with an inverted index file generated by the automatic indexing. On the other hand the second-level search exploits AKN based on the degree of asslxiation between terms. We have developed several formulas for rearranging the rank of texts at second-level search, and evaluated the performance of the effects of them on resolving the word sense ambiguities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.75-77
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• 2021

A variety of search engines are currently in use. The search engine supports the retrieval of data required by users through three stages: crawling, index generation, and output of search results based on meta-tag information. However, a large number of documents obtained by searching for keywords are often unrelated or scarce. Because of these problems, it takes time and effort to grasp the content from the search results and classify the accuracy. The index of search engines is updated periodically, but the criteria for weighted values and update periods are different from one search engine to another. Therefore, this paper uses the LSTM model, which extracts the relationship between keywords entered by the user and documents instead of the existing search engine, and improves the relationship between keywords and documents by entering keywords that the user wants to find.