• Journal of the Korean Society for Library and Information Science
• /
• v.8
• /
• pp.73-101
• /
• 1981

Retrieval effectiveness is the principal criterion for measuring the performance of an information retrieval system. The effectiveness of a retrieval system depends primarily on the extent to which it can retrieve wanted documents without retrieving unwanted ones. So, ultimately, effectiveness is a function of the relevant and nonrelevant documents retrieved. Consequently, 'relevance' of information to the user's request has become one of the most fundamental concept encountered in the theory of information retrieval. Although there is at present no consensus as to how this notion should be defined, relevance has been widely used as a meaningful quantity and an adequate criterion for measures of the evaluation of retrieval effectiveness. The recall and precision among various parameters based on the 'two-by-two' table (or, contingency table) were major considerations in this paper, because it is assumed that recall and precision are sufficient for the measurement of effectiveness. Accordingly, different concepts of 'relevance' and 'pertinence' of documents to user requests and their proper usages were investigated even though the two terms have unfortunately been used rather loosely in the literature. In addition, a number of variables affecting the recall and precision values were discussed. Some conclusions derived from this study are as follows: Any notion of retrieval effectiveness is based on 'relevance' which itself is extremely difficult to define. Recall and precision are valuable concepts in the study of any information retrieval system. They are, however, not the only criteria by which a system may be judged. The recall-precision curve represents the average performance of any given system, and this may vary quite considerably in particular situations. Therefore, it is possible to some extent to vary the indexing policy, the indexing policy, the indexing language, or the search methodology to improve the performance of the system in terms of recall and precision. The 'inverse relationship' between average recall and precision could be accepted as the 'fundamental law of retrieval', and it should certainly be used as an aid to evaluation. Finally, there is a limit to the performance(in terms of effectiveness) achievable by an information retrieval system. That is : "Perfect retrieval is impossible."

• Journal of KIISE:Software and Applications
• /
• v.31 no.10
• /
• pp.1374-1385
• /
• 2004

The relevance feedback process uses information obtained from a user about an initially retrieved set of documents to improve subsequent search formulations and retrieval performance. In the extended Boolean model, the relevance feedback Implies not only that new query terms must be identified, but also that the terms must be connected with the Boolean AND/OR operators properly Salton et al. proposed a relevance feedback method for the extended Boolean model, called the DNF (disjunctive normal form) method. However, this method has a critical problem in generating a reformulated queries. In this study, we investigate the problem of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve the problem. We show the results of experiments which are performed on two data sets: the DOE collection in TREC 1 and the Web TREC 10 collection.

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

• Journal of the Semiconductor & Display Technology
• /
• v.7 no.4
• /
• pp.13-18
• /
• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.3
• /
• pp.533-540
• /
• 2012

In this paper, we propose a document summarization method using the pseudo relevance feedback and the term weighting based on semantic features. The proposed method can minimize the user intervention to use the pseudo relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature. In addition, it uses the semantic feature of term weighting and the expanded query to reduce the semantic gap between the user's requirement and the result of proposed method. The experimental results demonstrate that the proposed method achieves better performant than other methods without term weighting.

• Journal of Broadcast Engineering
• /
• v.5 no.1
• /
• pp.61-67
• /
• 2000

Generally, relevance, feedback reflecting user's intention has been used to refine the refine the query conditions in image retrieval. However, in this paper, the usage of the relevance feedback is extended to the image database categorization so as to be accommodated to the user independent image retrieval. In our approach, to guarantee a desirable user-satisfactory performance descriptors and the elements of the descriptors corresponding unique features associatiated with of each image are weighted using the relevance feedback where experts can more lead rather than beginners do. In this paper, we propose a proper image description scheme consisting of global information, local information, descriptor weights and element weights based on color and texture descriptors. In addition, we also introduce an appropriate learning method based on the reliability scheme preventing wrong learning from abusive feedback.

• Journal of the Korean Society for information Management
• /
• v.10 no.2
• /
• pp.3-22
• /
• 1993

In recent years various document ranking methods such as Relevance. R-Distance and K-Distance have been developed wh~ch can be used in thesaurus-based boolean retrieval systems. They give high quality document rankings in many cases by using term dependence lnformatlon from a thesaurus. However, they suffer from several problems resulting from inefficient and Ineffective evaluation of boolean operators AND. OR and NOT. In this paper we propose new thesaurus-based document ranking methods called KB-FSM and KB-EBM by exploitmg the enhanced fuzzy set model and the extended boolean model. The proposed methods overcome the problems of the previous methods and use term dependencies from a thesaurs effectively. We also show through performance comparison that KB-FSM and KBEBM provide higher retrieval effectiveness than Relevance. R-D~stance and K-Distance.

• Journal of the Korea Society of Computer and Information
• /
• v.4 no.1
• /
• pp.21-27
• /
• 1999

In this paper. we propose a new method for automatic classification of documents using the degree of similarity between words. First, we seek relevance terms using automatic indexing. Second, we found frequency in use words in documents and the degree of relevance between the words using probability model. Continuously, we extracted the set of words which is connected the relevance closely and created the profiles characterizing each classification And, with the profile we finally classified them. We experimented on classifying two groups of documents. Some documents were about Genetic Algorithm. The others were about Neural Network. The results of the experiments indicated that automatic classification with word accordance of degree enable us to manage the retrieved documents structurally.

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

• Journal of Information Technology Applications and Management
• /
• v.18 no.4
• /
• pp.1-20
• /
• 2011

The recent development of mobile devices, including smartphones has maximized the user's convenience. This phenomenon has triggered the specification and activation of mobile cloud service and transformed the user's information usage and communication means. Despite such development of new technology, there is little understanding of mobile cloud. Therefor, in order to help the understanding of the usage of mobile cloud computing technology, this study developed the research model based on the UTAUT (the Unified Theory of Acceptance and Usage of Technology) model. The proposed research model included five external determinants, such as Interoperability, Information Organization, Adequacy, Information Security and Context Awareness, along with the moderating effects of Job Relevance, and empirically proved their influence on the user's job performance. This study proved the research model by analyzing the data collected from 287 copies of survey with the structural equation modeling approach. The result showed that all determinants, except Information Security have positive impact on the usage of mobile cloud computing technology. Thus, this study can compliment the limitations of previous research, suggest a new direction to analyze the user's perspective in the related research on mobile cloud and build a theoretical foundation for the further development of mobile cloud.