• The Journal of Society for e-Business Studies
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• v.14 no.2
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• pp.95-116
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• 2009

Ontologies have provided supports in integrating heterogeneous and distributed information. More and more ontologies and tools have been developed in various domains. However, building ontologies requires much time and effort. Therefore, ontologies need to be shared and reused among users. Specifically, finding the desired ontology from an ontology repository will benefit users. In the past, most of the studies on retrieving and ranking ontologies have mainly focused on lexical level supports. In those cases, it is impossible to find an ontology that includes concepts that users want to use at the semantic level. Most ontology libraries and ontology search engines have not provided semantic matching capability. Retrieving an ontology that users want to use requires a new ontology selection and ranking mechanism based on semantic similarity matching. We propose an ontology selection and ranking model consisting of selection criteria and metrics which are enhanced in semantic matching capabilities. The model we propose presents two novel features different from the previous research models. First, it enhances the ontology selection and ranking method practically and effectively by enabling semantic matching of taxonomy or relational linkage between concepts. Second, it identifies what measures should be used to rank ontologies in the given context and what weight should be assigned to each selection measure.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.315-322
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• 2008

It is important to retrieve effectively the visual media such as pictures and video in the internet, especially to the application areas such as electronic art museum, e-commerce, and internet shopping malls. It is also needed in these areas to have content-based or even semantic-based multimedia retrieval instead of simple keyword-based retrieval. In our earlier research, we proposed a semantic-based visual media retrieval framework for the effective retrieval of the visual media from the internet. It uses visual media metadata and ontology based on the web service to achieve the semantic-based retrieval. In this research, there are more than one visual media service providers and one central service broker. As a preliminary step to the visual media data retrieval, a method is proposed to retrieve the service providers effectively. The method uses the structure of the ontology tree to obtain the providers and their rankings. It also uses the size of sub nodes and child nodes in the tree. It measures the rankings of providers more effectively than previous method. The experimental results show the accuracy of the method while keeping compatible speed against the existing method.

• Journal of Digital Contents Society
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• v.15 no.5
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• pp.603-612
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• 2014

To provide efficient and correct search results, ontology which use the structure of information, is considered as a main mechanism in the semantic web. Therefore, recent research in information retrieval and data construction have emphasized the use of ontologies as a data representation and search mechanism. In this paper, we propose a semantic search method using ontology to improve search ability in web environment. Ontology and knowledge base is used to represent semantic meaning of the data and provide related web documents and facts as results. Also, search result ranking mechanism is proposed. The mechanism use cardinality of the keyword in the contents and structural information of ontology. Experimental results with several query processing indicate that different coefficient value in the expression gives different results in sample ontology system and we propose appropriate values of the coefficient.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.271-277
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• 2013

Typical semantic search query for Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge base represented logically. Existing keyword-based retrieval system is Preference for the semantic interpretation of a user's query is not the meaning of the user keywords of interconnect, you can not search. In this paper, we propose a method that can provide accurate results to meet the user's search intent to user preference based evaluation by ranking search. The proposed scheme is Integrated ontology-based knowledge base built on the formal structure of the semantic interpretation process based on ontology knowledge base system.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.34-36
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• 2003

지능형 웹 서비스를 효과적으로 구현하기 위해서는 다양한 사용자들이 필요로 하는 데이터를 만족스럽게 제공할 수 있는 매치 메이킹의 구현이 중요한 과제이다. 이를 위한 관련 연구로 필터링 메커니즘을 제안하고 있는 LARKS, 브로커에이전트를 이용한 InfoSlueth, RDF 그래프 매칭 연구 및 DL 기반의 매칭 방법 등이 있다. 그러나 기존 연구들은 등급 개념을 가지는 유연한 검색 결과를 제공하지 못한다는 큰 문제점을 가진다. 본 논문에서는 기존 방법들을 개선하기 위한 노력으로서. 서비스 매치 메이킹의 결과들에 등급(랭킹)을 부터 하는 시맨틱 매치 메이커를 제안한다. 본 논문에서 제안하는 시맨틱 매치 메이커는 서비스 제공자와 서비스 요청자 사이의 유연한 매칭을 제공하여 지능형 웹 서비스를 효과적으로 수행 할 수 있게 한다. 본 논문에서 제안한 방법론은 서비스 광고 및 요청을 표현하기 위한 언어로 DAML-S를 채택하였고. DAML-S의 서비스 프로파일 뿐만 아니라 프로세스 모델 온톨로지 모두를 고려하는 새로운 접근법이다.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.43-65
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• 2007

Automatic recommendation system of panel pool should be designed to support universal, expertness, fairness, and reasonableness in the process of review of proposals. In this research, we apply the theory of probabilistic ontology to measure relatedness between terms in the classification of academic domain, enlarge the number of review candidates, and rank recommendable reviewers according to their expertness. In addition, we construct a researcher network connecting among researchers according to their various relationships like mentor, coauthor, and cooperative research. We use the researcher network to exclude inappropriate reviewers and support fairness of reviewer recommendation process. Our methodology recommending proper reviewers is verified from experts in the field of proposal examination. It propose the proper method for developing a resonable reviewer recommendation system.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.167-179
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• 2009

This paper introduces techniques to recommend standardized tags using tag ontology. Tag recommendation consists of TWCIDF and TWCITC; the former technique automatically tags a large quantity of already existing document groups, and the latter recommends tagging for new documents. Tag groups are created through several processes, including preprocessing, standardization using tag ontology, automatic tagging and defining ranks for recommendation. In the preprocessing process, in order to search semantic compound nouns, words are combined to establish basic word groups. In the standardization process, typographical errors and similar words are processed. As a result of experiments conducted on the basis of techniques presented in this paper, it is proved that real-time automatic tagging and tag recommendation is possible while guaranteeing the accuracy of tag recommendation.