• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.95-105
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• 2009

There are various studies to provide useful information for users on huge data of web-sites. Web usage mining among them is a method to extract meaningful patterns based on web users' log data. Most of existing patterns of web usage mining, however, had not considered users' diverse inclination but created general models. Web users' keywords can have various meaning upon their tendency and background knowledge. This study is for generating Multi Concept Keyword Model (MCK-Model) by analyzing web usage information on users' keywords of interest. MCK-Model can supply web page network for various inclination based on users' keywords of interest. Also, MCK-Model can be used to recommend the most proper web pages and it has been confirmed that the suggested method is useful enough.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.205-214
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• 2016

As a large amount of graph-structured data is widely used in various applications such as social networks, semantic web, and bio-informatics, keyword-based search over graph data has been getting a lot of attention. In this paper, we propose an efficient method for keyword search over graph data to find a set of top-k answers that are relevant as well as non-redundant in structure. We define a non-redundant answer structure for a keyword query and a relevance measure for the answer. We suggest a new indexing scheme on the relevant paths between nodes and keyword terms in the graph, and also propose a query processing algorithm to find top-k non-redundant answers efficiently by exploiting the pre-calculated indexes. We present effectiveness and efficiency of the proposed approach compared to the previous method by conducting an experiment using a real dataset.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.879-886
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• 2018

The basic search engine on Twitter shows not only tweets that contain search keywords, but also all articles written by users with nicknames containing search keywords. Since the tweets unrelated to the search keyword are exposed as search results, it is inconvenient to many users who want to search only tweets that include the keyword. To solve this inconvenience, this study improved the search function of Twitter by developing an algorithm that searches only tweets that contain search keywords. The improved functionality is implemented as a Web service using ASP.NET MVC5 and is available to many users. We used a powerful collection method in C# to retrieve the results of an object, and it was also possible to output them according to the number of 'retweets' or 'favorites'. If the number of retrieved numbers is less than a given number, we also added an exclusion filter function. Thus, sorting search results by the number of retweets or favorites, user can quickly search for opinions that are of interest to many users. It is expected that many users and data analysts will find the developed function convenient to search on Twitter.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.357-364
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• 2005

In this paper, we propose an accurate and fast keyword spotting system for searching user-specified keyword in Hangul document images by using two-level image-to-image matching. The system is composed of character segmentation, creating a query image, feature extraction, and matching procedure. Two different feature vectors are used in the matching procedure. An experiment using 1600 Hangul word images from 8 document images, downloaded from the website of Korea Information Science Society, demonstrates that the proposed system is superior to conventional image-based document retrieval systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.337-340
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• 2000

Using the Information Retrieval systems on the Internet, the demand of exact and specific information has also been popularized. To offer exact information, there k3 been generalized demand of searching from the keyword of the shortened text and also of the full-text. This study is to suggest a scheme for full-text searches. It is to compare the existing scheme of information search and full-text information search with interMedia text. We suggest search methods for the full-text.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.3
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• pp.103-110
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• 2008

This paper proposes an effective keyword extraction method for the Web videos. The proposed method classifies the Web video pages in one of 4 types. As such, we analyzed the structure of the Web pages based on the number of videos and the layout of the Web pages. And then we applied the keyword extraction algorithm fit to each page type. The experiment with 1,087 Web pages that have total 2,462 videos showed that the recall of the proposed extraction method is 18% higher than ImagerRover[2]. So, the proposed method could be used to build a powerful video search system for WWW.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.901-910
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• 2003

Generally, most commercial retrieval engines adopt boolean query as user's query type. Although boolean query is useful to retrieval engines that need fast retrieval, it is not easy for user to express his demands with boolean operators. So, many researches have been studied for decades about information retrieval systems using natural language query that is convenient for user. To retrieve documents that are suitable for user's demands, they have to express their demands correctly, So, this thesis proposes interactive query process agent using natural language. This agent expresses demands concrete through gradual interaction with user, When users input a natural language Query, this agent analyzes the query and generates boolean query by selecting proper keyword and feedbacks the state of the keyword selected. If the keyword is a synonymy or a polysemy, the agent expands or limits the keyword through interaction with user. It makes user express demands more concrete and improve system performance. So, this agent can improve the precision of Information Retrieval.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.538-550
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• 2006

XML Keyword search enables us to get information easily without knowledge of structure of documents and returns specific and useful partial document results instead of whole documents. Element level query processing makes it possible, but computational complexity, as the number of documents grows, increases significantly overhead costs. In this paper, we present document-level ranking scheme over XML documents which predicts results of element-level processing to reduce processing cost. To do this, we propose the notion of 'keyword proximity' - the correlation of keywords in a document that affects the results of element-level query processing using path information of occurrence nodes and their resemblances - for document ranking process. In benefit of document-centric view, it is possible to reduce processing time using ranked document list or filtering of low scored documents. Our experimental evaluation shows that document-level processing technique using ranked document list is effective and improves performance by the early termination for top-k query.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.875-878
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• 2007

Up until now, when a user search product information, the keyword-based search that mainly uses frequency of words or vocabulary information has been utilized in large. In the keyword-based research, the user should have to bear additional burden in order to search the displayed results manually once again because it shows those files that have no connection at all with the inquiries made by the user. To resolve such a problem, ontology has been emerged. In this paper, product search system using ontology was constructed directly and also tested how accurate search it does perform through the searching according to classification. To test this, about 40,000 product data of A discount store, which was operating on/off line discount stores, were constructed as database, and developmental environment for User Interface was tested by having developed the search system using JSP and PowerBuilder 9.0. Results from the test proved that the search method using Domain Ontology for product presented and designed in this paper was superior to the existing keyword-based search method.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.343-362
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• 2007

The purpose of this study is to design and implement an ontology-based retrieval system for the electronic records of universities and to compare its performance with the existing keyword-based retrieval system. We used OntoStudio 1.4 for implementing an ontology-based retrieval system, and the test collection consisted of the following: (1) 5,099 electronic records of the 'personnel management notification' created by Korea Maritime University, (2) 20 topics (10 short-topics and 10 long-topics), and (3) the relevant assessments were conducted by the group of human experts. 10 university staff participated in the experiment of keyword-based searching and used the same test collection as used in the experiment of ontology-based searching. The ontology-based retrieval system outperformed to the keyword-based retrieval system in terms of Recall and Precision, and the same results showed in the test of the short-topics and long-topics comparison.