• The Journal of Society for e-Business Studies
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• v.8 no.2
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• pp.23-31
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• 2003

Link-based search engines generate the rank using linked information of related web-documents . HITS(Hypertext Internet Topic Search), representative ranking evaluation algorithm using a special feature of web-documents based on such link, evaluates the importance degree of related pages from linked information and presents by ranking information. Problem of such HITS algorithm only is considered the link frequency within documents and depends on the set of web documents as input value. In this paper, we design the search agent based on better HITS algorithm according to advanced suitability between query and search-result in the set of given documents from link-based web search engine. It then complements locality of advanced search performance and result.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.1
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• pp.247-262
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• 2011

Although research has shown a significant amount of work identifying various dimensions of relevance along with exhaustive lists of relevance criteria, there seem to have been less effort to apply the findings to improve actual systems design. Based on this assumption, this paper investigates to what extent those relevance criteria have been incorporated into the interface features of major commercial Web search engines, suggesting what can/should be done more. Before stepping into the actual system features, this paper compares recent relevance research in Information Science with other human factor studies both in Information Science and its neighboring discipline (HCI), as an attempt to identify studies that are conceptually similar to the relevance research, but not named as such way. Similarities and differences between these studies are presented. Recommendations suggested to support applicable interface features include: 1) further personalization of interface designs; 2) author-supplied meta tags for the Web contents; and 3) extensions of beyond-topical representations based on link structure.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.5
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• pp.14-28
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• 2009

The PageRank algorithm is an important component for ranking Web pages in Google and other search engines. While many improvements for the original PageRank algorithm have been proposed, it is unclear which variations (and their combinations) provide the "best" ranked results. In this paper, we evaluate the ranking quality of the well-known variations of the original PageRank algorithm and their combinations. In order to do this, we first classify the variations into link-based approaches, which exploit the link structure of the Web, and knowledge-based approaches, which exploit the semantics of the Web. We then propose algorithms that combine the ranking algorithms in these two approaches and implement both the variations and their combinations. For our evaluation, we perform extensive experiments using a real data set of one million Web pages. Through the experiments, we find the algorithms that provide the best ranked results from either the variations or their combinations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.1028-1034
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• 2004

As use of internet generalized laying stress on WWW(World Wide Web) service of multimedia based recently, we could acquire many informations that exist to all over the world's computer network. It is risen to important problem that use of internet acquires correct information rapidly on modem society which is generalized. This paper designed internet search engine and understand structure of that drawing URL which is optimized, and secure embodiment technology using OOPL(Object-Oriented Programming Language). Also, compare with existent domestic manufacture search engines and system that propose showed that the bad link rate is improved in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.797-804
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• 2002

As internet use is being generalized central of WWW(World Wide Web) service of multimedia based recently, we could acquire many informations that exist to all over the world's computer network .Therefore, picking up of information became important problem before that internet is generalized, but it is risen to important problem to acquire correct information rapidly on modem society that use of internet is generalized. This paper designed internet search engine and understand structure of internet search engine drawing URL that is optimized, and secure embodiment technology using Java that is language of object base. Search engine that proposed in this paper maintained user's the convenience by offer keyword search, and simplify user interface And although quantity of searched information site is few, search engine show that the bad link rate of searched result is improved compare with existent domestic manufacture search engines.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.357-365
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• 2010

Personalized web search engine provides personalized results to users by query expansion, re-ranking or other methods representing user's intention. The personalized result page includes URL, page title and small text fragment of each web document. which is known as snippet. The snippet is the summary of the document which includes the keywords issued by either user or search engine itself. Users can verify the relevancy of the whole document using only the snippet, easily. The document summary (snippet) is an important information which makes users determine whether or not to click the link to the whole document. Hence, if a search engine generates personalized document summaries, it can provide a more satisfactory search results to users. In this paper, we propose a personalized document summary system for personalized web search engines. The proposed system provides increased degree of satisfaction to users with marginal overhead.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.26-31
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• 2005

In this paper, we present the document summarizers which are simpler and more condense than the existing ones generally used in the web search engines. This method is a statistic-based summarization method using the concept of the complete graph. We suppose that each sentence as a vertex and the similarity between two sentences as a link of the graph. We compare this summarizer with those of Clustering and MMR techniques which are well-known as the good summarization methods. For the comparison, we use FScore using the summarization results generated by human subjects. Our experimental results verify the accuracy of this method, being about $30\%$ better than the others.

• Journal of Korean Library and Information Science Society
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• v.51 no.1
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• pp.115-143
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• 2020

With the era of the 4th Industrial Revolution, the number of data-centric integrated researches increases. The integrated researches make information identification and linkage more important, so it is necessary to seek a method to efficiently manage and share academic-information for supporting the researches. Therefore, this study aims to analyze identification system and linkable information types of 12 major academic search engines and bibliographic databases(ASEBDs) in Korea and abroad and to propose a method to identify and link academic-information. The analysis was conducted 2 times, and academic-information types, searchable fields, linkable information types, used identification system were investigated. As a result, the ASEBDs link directly or/and indirectly 3~4 information types based on their own identifiers with persistent identifiers. In addition, they identify academic-information semi-automatically based on machine learning methodology and collect and manage the related data. Finally, the method for academic-information linkage was proposed in terms of practice and society: linkage based on persistent identifiers and linkage based on collaborative network of institutions.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.745-754
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• 2002

The Google search site (http://www.google.com), which was introduced in 1998, implemented the PageRank algorithm for the first time. PageRank is a ranking method based on the link structure of the Web pages. Even though PageRank has been implemented and being used in various commercial search engines, implementation details did not get documented well, primarily due to business reasons. Implementation techniques introduced in [4,8] are not sufficient to produce PageRank values of Web pages. This paper explains the techniques[4,8], and suggests major data structure and four implementation techniques in order to apply the PageRank algorithm. The paper helps understand the methods of applying PageRank algorithm by means of showing a real system that produces PageRank values of Web pages.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.