• Korean Management Science Review
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• v.28 no.1
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• pp.1-10
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• 2011

One of the fundamental roles of web object navigation is to support what the user wants precisely and efficiently from the enormous web database to the web browser. As long as the web search results are a set of individual lists, it is all right to display each and every web result for the web browser to display a web object one by one. However, in case the search results are a collection of multiple interrelated web objects, then there is a need to represent for a new mechanism for linked web objects at a time. We define a unit of web objects derived from a Steiner tree where the web objects include a set of specific keywords calculated by the weight from which the solutions are extracted. Even if a web object does not include all the keywords, then the related hypertext linked web objects are derived and displayed onto the mobile web browser with meta data in one shot. In this paper, it is applied for the mobile browser that the web contents can dynamically be displayed with Steiner trees until each renewal of the navigation request may be issued. In this paper, a new synchronized mobile browsing method is developed so that the navigating time can drastically be reduced and the web navigating efficiency can be dramatically enhanced without sacrificing memory consumption.

• The Journal of Korean Association of Computer Education
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• v.13 no.1
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• pp.45-52
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• 2010

With rapid increase in the number of web documents, the problem of information overload in Internet search is growing seriously. In order to improve web search results, previous research studies employed user queries/preferred words and the number of links in the web documents. In this study, performance of the search results exploiting these two criteria is examined and other preference criteria for web documents are analyzed. Experimental results show that personalized web search results employing queries and preferred words yield up to 1.7 times better performance over the current search engine and that the search results using the number of links gives up to 1.3 times better performance. Although it is found that the first of the user's preference criteria for web documents is the contents of the document, readability and images in the document are also given a large weight. Therefore, performance of web search personalization algorithms will be greatly improved if they incorporate objective data reflecting each user's characteristics in addition to the number of queries and preferred words.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.880-886
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• 2016

With lots of information occurring on the Internet, the search engine plays a role in gathering the scattered information on the Internet. Some search engines show not only search result pages including search keyword but also search result numbers of the keyword. The number of keyword searching result provided by the Google search engine can be utilized to identify overall trends for this search word on the internet. This paper is aimed designing and realizing the system which can efficiently manage the number of searching result provided by Google search engine. This paper proposed system operates by Web, and consist of search agent, storage node, and search node, manage keyword and search result, numbers, and executing search. The proposed system make the results such as search keywords, the number of searching, NGD(Normalized Google Distance) that is the distance between two keywords in Google area.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.229-236
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• 2005

The Semantic Web enables machines to achieve an effective retrieval, integration, and reuse of web resources. The keyword search method currently used has a limit to accurate search results because of a simple string matching method in web environment. This paper proposes an Ontology-Based Information Retrieval which can solve the problems and retrieve better search results through semantic relations. In this system, we implemented the Cultural Assets Ontology based on OWL with RDQL and Jena API. we also suggest a method to handle properties stored in a database.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.345-348
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• 2005

Because the current web search engine estimates the similarity of documents, using the frequency of words, many documents irrespective of the user query are provided. To solve these kinds of problems, the semantic web is appearing as a future web. It is possible to provide the service based on the semantic web through ontology which specifies the knowledge in a special domain and defines the concepts of knowledge and the relationships between concepts. In this paper to search the information of potential customers for home-delivery marketing, we model the specific domain for generating the ontology. And we research how to retrieve the information, using the ontology. Therefore, in this paper, we generate the ontology to define the domain about potential customers and develop the search robot which collects the information of customers.

• Journal of Science and Technology Studies
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• v.13 no.1
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• pp.181-216
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• 2013

This paper tries to analyze technologies of search engine generally, and reveal the additional modes of Korean search engine at the same time. Recently it said that search engine becomes a self-moving and is getting more strong power than the former one existed. There are many difference interpretative views from technological determination to instrumentalism surrounding this system. Search engine invents the technological mode that draws user's attention to make its own expertise reinforce. It is stemmed from the rationality of its own. Especially Korean search engine exposed unique mutation as self-proliferation of it during past a decade, as for example "related keyword" or "real-time popular keyword" service. Its automatic decision aroused democracy matter, now it is not only web guide. How we do make it to serve in democracy, accepting the independent expertise of it simultaneously? We might find new prospect when focusing on interactional modality between engine and human actor, instead counting both as a separate one.

• The Research Journal of the Costume Culture
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• v.9 no.3
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• pp.412-432
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• 2001

Internet search engines are used by the majority of find information on the Web. However, Web users can be often dissatisfied with the mistakes in the retrieval of ‘Fashion’ information from the Internet. The purpose of this study is to analyze the ‘Fashion’ category structure in the Internet search engines. There are 2 steps for achieving it: the first, to investigate the structures of ‘Fashion’ categories and then, to analyze the gap between ‘Fashion’ categories defined by them and extensive ‘Fashion’categories, which are approached on 2 sides of the fashion-life and fashion-business. We select 5 major search engines for the case study: Yahoo, Lycos, Naver, Hanmir, Empas, which ranked as top 5 of total search engines and potal sites in February, 2001, and retrieve ‘Fashion’ categories from the first level to the last level by using both “topics retrieval”. Eventually, we can find the problems of ‘Fashion’ category structure in search engines. Also, it is concluded with a brief perspective of ‘Fashion’ categories in the Internet search engines and the implications for the future.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1986-1989
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• 2003

This paper proposes the information retrieval improvement for the Web using the structure and hyperlinks of HTML documents along with user profile. The method bases on the rationale that terms appearing in different structure of documents may have different significance in identifying the documents. The method partitions the occurrence of terms in a document collection into six classes according to the tags in which particular terms occurred (such as Title, H1-H6 and Anchor). We use genetic algorithm to determine class importance values and expand user query. We also use this value in similarity computation and update user profile. Then a genetic algorithm is used again to select some terms from user profile to expand the original query. Lastly, the search engine uses the expanded query for searching and the results of the search engine are scored by similarity values between each result and the user profile. Vector space model is used and the weighting schemes of traditional information retrieval were extended to include class importance values. The tested results show that precision is up to 81.5%.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.737-744
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• 2004

Retrieving relevant information well corresponding to the user`s request from web is a crucial task of search engines. However, most of conventional search engines based on pattern matching schemes to queries have a limitation that is not easy to provide results corresponding to the user`s request due to the uncertainty of queries. To overcome the limitation in this paper, we propose a framework for knowledge-based semantic meta-search engines with the following five processes: (i) Query formation, (ii) Query expansion, (iii) Searching, (iv) Ranking recreation, and (v) Knowledge base. From simulation results on english-based web documents, we can see that the Proposed knowledge-based semantic meta-search engine provides more correct and better searching results than those obtained by using the Google.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.215-221
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• 2003

Generally, we want to be searched the newest as well as some appropriate personalized information from the internet resources. However, it is a complex and repeated procedure to search some appropriate information. Moreover, because the user's interests are changed as time goes, the real time modeling of a user's interests should be necessary. In this paper, I propose PREA system that can search and filter documents that users are interested from the World Wide Web. And then it constructs the user's interest model by a modified spreading neural network. Based on this network, PREA can easily produce some queries to search web documents, and it ranks them. The conventional spreading neural network does not have a visualization function, so that the users could not know how to be configured his or her interest model by the network. To solve this problem, PREA gives a visualization function being shown how to be made his interest user model to many users.