• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.389-403
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• 2008

As the information of web dramatically increase, the existing web reveals more and more limitations in information search because web pages are designed only for human consumption by mixing content with presentation. In order to improve this situation, the Semantic Web comes on the stage by W3C. Semantic web is based on ontology that defines relationships between resources and it is enough to bring a significant advancement in web search. But to do this, the Semantic Web must provide a novel search and visualization methods which can make users instantly and intuitively understand why and how the results are retrieved because ontology has formal explicit descriptions of meaning. In this paper, we propose a semantic association-based search methodology that consists of how to find relevant information for a given user's query in the ontology, that is, a semantic network of resources and properties and how to provide proper visualization and navigation methods on the results. From this work, users can search the semantically associated resources for their query and also navigate such associations between resources.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.346-355
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• 2005

In this paper, we propose a meta-search engine which provides distributed product information through a unified access to multiple e-commerce. The meta-search engine proposed in this paper performs the following functions: (I) The user is able to create a category-based user query, (2) by using the WordNet, the query is semantical refined fined for increasing search accuracy, and (3) the meta-search engine recommends an e-commerce site which has the closest product information to the user's search intention, by matching the user query with the product catalogs in the e-commerce sites linked to the meta-search engine. An experiment shows that the performance of our model is better than that of general keyword-based search.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.245-252
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• 2014

Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge base represented logically. But, the ordinary users don't know well the complex formal query language and schema of the knowledge base. So, the system should interpret the meaning of user's keywords. In this paper, we describe a user query interpretation system for the semantic retrieval of multimedia contents. Our system is ontological knowledge base-driven in the sense that the interpretation process is integrated into a unified structure around a knowledge base, which is built on domain ontologies.

• 한국노년학
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• v.39 no.1
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• pp.109-125
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• 2019

This study examined factors that prevent older adults from using computers and/or smartphones for their health information search and health management. This study conducted face-to-face survey of a total of 240 older adults aged over 65. Based on the responses of open-ended questions, this study conducted semantic network analysis. The results showed that low level of perceived usefulness(PU) (e.g., information I want to find, detailed information, and trustworthiness) and perceived ease of use(PEOU) (e.g., how to search for information, how to install applications, and visibility) are main factors that prevent older adults from using computers and/or smartphones for their health information search and health management. Furthermore, based on the results of semantic network analysis, further hierarchical regression analysis confirmed that PU and PEOU were positively associated with intention to use mobile application. Thus, the results imply that increasing older adults' PU and PEOU can promote their intention to use mobile application.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.516-523
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• 2008

Recently, the Web has been constructed based on collective intel1igence and growing up quickly. User created contents have been made the mainstream in this environments. So it's required to make an efficient technique of searching for the contents. The current searching technique mainly is achieved by key words. Semantic Web based on similarity and relationship of a language and using user tags in web2.0 also have been researched with activity. Generally, the web of the participation architecture has a lot of user created contents, various forms and classification. Therefore, it is necessary to classify and to efficiently search for a lot of user created contents. In this paper, we propose a depth searching technique considering the relationship among the tags that descript user contents. It is expected that the proposed depth searching techniques can reduce the time taken to search for the unwanted contents and the increase the efficiency of the contents searching using a service of suggestion words in tags groups.

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.129-136
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• 2006

Information Retrieval System aims towards providing fast and accurate information to users. However, current search systems are based on plain svntactic analysis which makes it difficult for the user to find the exact required information. This paper proposes the SW-IRS (Semantic Web-based Information Retrieval System) using an Ontology Server. The proposed system is purposed to maximize efficiency and accuracy of information retrieval of unstructured and semi-structured documents by using an agent-based automatic classification technology and semantic web based information retrieval methods. For interoperability and easy integration, RDF based repository system is supported, and the newly developed ranking algorithm was applied to rank search results and provide more accurate and reliable information. Finally, a new ranking algorithm is suggested to be used to evaluate performance and verify the efficiency and accuracy of the proposed retrieval system.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.93-102
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• 2007

Metadata and ontology can be used to retrieve related information through the inference mure accurately and simply on the Semantic Web. RDF and RDF Schema are general languages for representing metadata and ontology. An enormous number of keywords on the Semantic Web are very important to make practical applications of the Semantic Web because most users prefer to search with keywords. In this paper, we consider a resource as a unit of query results. And we classily queries with keyword conditions into three patterns and propose indexing techniques for keyword-search considering both metadata and ontology. Our index maintains resources that contain keywords indirectly using conceptual relationships between resources as well as resources that contain keywords directly. So, if user wants to search resources that contain a certain keyword, all resources are retrieved using our keyword index. We propose a structure of table for storing RDF Schema information that is labeled using some simple methods.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.241-252
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• 2011

During the last decades, other parts of operating systems, storage devices, and media are changed steadily, whereas filesystem is changed little. As data is grown bigger, the number of files to be managed also increases in geometrically. Researches about new filesystem schemes are being done widely to support these files efficiently. In web document search area, there are many researches about finding meaningful documents using semantic search. Many researches tried to apply these schemes, which is been proven in web document search previously, to filesystems. But they've focused only on higher layer of filesystem, that is not related seriously to storage media. Therefore they're not well tuned to physical characteristics of new flash memory based SSD which has different features against traditional HDD. We enhance log structured filesystem, that is already well known to work better in SSD, by putting semantic search scheme to and with multi logging point.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.127-133
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• 2009

To ensure access to rapidly growing video collection, video indexing is becoming more and more essential. A database for video should be build for fast searching and extracting the accurate features of video information with more complex characteristics. Moreover, video indexing structure supports efficient retrieval of interesting contents to reflect user preferences. In this paper, we propose semantic video retrieval method based on user preference. Unlikely the previous methods do not consider user preferences. Futhermore, the conventional methods show the result as simple text matching for the user's query that does not supports the semantic search. To overcome these limitations, we develop a method for user preference analysis and present a method of video ontology construction for semantic retrieval. The simulation results show that the proposed algorithm performs better than previous methods in terms of semantic video retrieval based on user preferences.