• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.133-152
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• 2004

As the Internet facilitates the rapid increase of information availability, the study on SDI service that provides users with relevant document in a timely manner has been developed. However, the practical use of this service has been low. This thesis aims at analyzing the reasons for this and developing relevance feedback based SDI system to improve the performance of the existing SDI system. Experimental systems that are developed for this study are SDI system based on users' minimum intervention feedback, SDI system based on perfect automation feedback, and SDI system based on users' maximum intervention feedback. The fourth system that utilizes the traditional SDI system is also studied to evaluate the level of performance improvement of the newly developed three types of SDI system. As a result of this study, SDI system based on users' maximum intervention feedback showed greatest performance improvement. The next performance improvement happened in order of SDI system based on perfect automation feedback, SDI system based on users' minimum intervention feedback, and the traditional SDI system. Feedback based systems showed greater performance improvement as they went through more feedback processes.

• Journal of the Korean Society for information Management
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• v.10 no.2
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• pp.43-67
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• 1993

The primary purpose of the study is to improve the low preclslon often found In full-text retrleval systems. In order to enhance the low precision of full-text retrleval wh~le retaining ~ t s hgh recall, relevance feedback mechanisms based on probabilistic retrieval models (binary independence and two-Polsson Independence models) were employed. Thls paper investigates the effect of relevance feedback on the performance of full-text retrieval systems.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1832-1840
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• 1999

Since the existed Korean Web IR systems generally use boolean system, it is difficult to retrieve the information to be wanted at one time. Also, because of the feature that web documents have the frequent abbreviation and many links, the keyword extraction using the inverted document frequency extracts the improper keywords for adding ambiguous meaning problem. Therefore, users must repeat the modification of the queries until they get the proper information. In this paper, we design and implement the relevance feedback agent system for resolving the above problems. The relevance feedback agent system extracts the proper information in response to user's preferred keywords and stores these keywords in preference DB table. When users retrieve this information later, the relevance feedback agent system will search it adding relevant keywords to user's queries. As a result of this method, the system can reduce the number of modification of user's queries and improve the efficiency of the IR system.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.129-131
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• 1998

웹 에이전트는 사용자가 좀 더 손쉽게 웹 상의 정보를 얻을 수 있게 하는 것을 목표로 하는 인터넷 정보 검색 도구이다. 본 논문에서는 개인용 웹 에이전트 시스템에서 적합성 피드백(Relevance Feedback)에 의해 사용자의 취향을 학습하는 방법을 제시하고, 실험을 통하여 제시된 적합성 피드백에 의한 학습 방법이 사용자의 취향을 성공적으로 학습함을 보였다. 적합성 피드백을 혼용할 때와 각각 한가지만 사용할 때로 나누어 실험하였으며, 피드백이 진행되면서 검색결과의 정확도가 변화하는 정도를 관찰하였다.

• Journal of the Korean Society for information Management
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• v.2 no.1
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• pp.108-135
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• 1985

When an information retrieval system is implemented on-line, users can interact with the system to improve the searches. There are studies which achieved dramatic improvements in system effectiveness by using automatic relevance feedback, a technique for reformulating a patron query based on initial retrieval result. In this thesis, an automatic query modification model was applied to a controlled keyword system.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.362-371
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• 2005

Similarity indexing and searching are well known to be difficult in high-dimensional applications such as multimedia databases. Especially, they become more difficult when multiple features have to be indexed together. In this paper, we propose a novel indexing method called the GB-index that is designed to efficiently handle complex similarity queries as well as relevance feedback in high-dimensional image databases. In order to provide the flexibility in controlling multiple features and query objects, the GB-index treats each dimension independently The efficiency of the GB-index is realized by specialized bitmap indexing that represents all objects in a database as a set of bitmaps. Main contributions of the GB-index are three-fold: (1) It provides a novel way to index high-dimensional data; (2) It efficiently handles complex similarity queries; and (3) Disjunctive queries driven by relevance feedback are efficiently treated. Empirical results demonstrate that the GB-index achieves great speedups over the sequential scan and the VA-file.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.424-431
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• 2012

In information retrieval, search engine provide the rank of web page and the summary of the web page information to user. Snippet is a summaries information of representing web pages. Visiting the web page by the user is affected by the snippet. User sometime visits the wrong page with respect to user intention when uses snippet. The snippet extraction method is difficult to accurate comprehending user intention. In order to solve above problem, this paper proposes a new snippet extraction method using fuzzy implication operator and relevance feedback. The proposed method uses relevance feedback to expand the use's query. The method uses the fuzzy implication operator between the expanded query and the web pages to extract snippet to be well reflected semantic user's intention. The experimental results demonstrate that the proposed method can achieve better snippet extraction performance than the other methods.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.141-152
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• 2005

A new emotion-based image retrieval method is proposed in this paper. Query emotion descriptors called query color code and query gray code are designed based on the human evaluation on 13 emotions('like', 'beautiful', 'natural', 'dynamic', 'warm', 'gay', 'cheerful', 'unstable', 'light' 'strong', 'gaudy' 'hard', 'heavy') when 30 random patterns with different color, intensity, and dot sizes are presented. For emotion image retrieval, once a query emotion is selected, associated query color code and query gray code are selected. Next, DB color code and DB gray code that capture color and, intensify and dot size are extracted in each database image and a matching process between two color codes and between two gray codes are peformed to retrieve relevant emotion images. Also, a new relevance feedback method is proposed. The method incorporates human intention in the retrieval process by dynamically updating weights of the query and DB color codes and weights of an intra query color code. For the experiments over 450 images, the number of positive images was higher than that of negative images at the initial query and increased according to the relevance feedback.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.137-142
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• 2012

Snippet is a summaries information of representing web pages which search engine provides user. Snippet and page rank in search engine abundantly influence user for visiting web pages. User sometime visits the wrong page with respect to user intention when uses snippet. The snippet extraction method is difficult to accurate comprehending user intention. In order to solve above problem, this paper proposes a new snippet extraction method using fuzzy association and pseudo relevance feedback. The proposed method uses pseudo relevance feedback to expand the use's query. It uses the fuzzy association between the expanded query and the web pages to extract snippet to be well reflected semantic user's intention. The experimental results demonstrate that the proposed method can achieve better snippet extraction performance than the other methods.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.193-205
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• 2007

Increasing amount of digital images requires more accurate and faster way of image retrieval. So far, image retrieval method includes content-based retrieval and keyword based retrieval, the former utilizing visual features such as color and brightness and the latter utilizing keywords which describe the image. However, the effectiveness of these methods as to providing the exact images the user wanted has been under question. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as a feedback during the retrieval session in order to define user’s need and provide improved result. Yet, the methods which have employed relevance feedback also have drawbacks since several feedbacks are necessary to have appropriate result and the feedback information can not be reused. In this paper, a novel retrieval model has been proposed which annotates an image with a keyword and modifies the confidence level of the keyword in response to the user’s feedback. In the proposed model, not only the images which have received positive feedback but also the other images with the visual features similar to the features used to distinguish the positive image are subjected to confidence modification. This enables modifying large amount of images with only a few feedbacks ultimately leading to faster and more accurate retrieval result. An experiment has been performed to verify the effectiveness of the proposed model and the result has demonstrated rapid increase in recall and precision while receiving the same number of feedbacks.