• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.5 no.1
• /
• pp.85-105
• /
• 1981

Information is an essential factor leading the rapid progress which is one of the distinguished characteristics in modem society. As more information is required and as more is supplied by individuals, governmental units, businesses, and educational institutions, the greater will be the requirement for efficient methods of communication. One possibility for improving the information dissemination process is to use computers. The capabilities of such machine are beginning to be used in the process of Information storage, retrieval and dissemination. An important problems, that must be carefully examined is whether one technique for information retrieval is better for worse than another. This paper examines problem of how to evaluate an information retrieval system. One specific approach is a cost accounting model for use in studying how to minimize the cost of operating a mechanized retrieval system. Through the use of cost analysis, the model provides a method for comparative evaluation between systems. The general cost accounting model of the literature retrieval system being designed by this study are given below. 1. The total cost accounting model of the literature retrieval system. The total cost of the literature retrieval system = (the cost per unit of user time X the amount of user time) + ( the cost per unit of system time X the amount of system time) 2. System cost accounting model system cost = (the pre-search system cost per unit of time X time) + (the search system cost per unit of time X time) + (the post search system cost per unit of time X time) 1) Pre-search system cost per unit of time = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 2) Search system cost per unit of time = comparison cost + document representation cost. 3) Post-search system cost per unit of time. = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 3. User cost accounting model Total user cost = [pre-search user cost per unit of time X (time + additional time) ] + [search user cost per unit of time X (time + additional time) ] + [post-search user cost per unit of time X (time + additional time) ].

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.4 s.36
• /
• pp.1-16
• /
• 2005

When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. Accordingly, this study suggests the re-ranking retrieval model which reflects the content based similarity between user's inquiry terms and index words by grasping the document knowledge structure. In order to accomplish this, the former constructs a thesaurus and similarity relation matrix to provide the subject analysis mechanism and the latter propose the algorithm which establishes a search model such as query expansion in order to analyze the user's demands. Therefore, the algorithm that this study suggests as retrieval utilizing the information structure of a retrieval system can be content-based retrieval mechanism to establish a 2-step search model for the preservation of recall and improvement of accuracy which was a weak point of the previous fuzzy retrieval model.

• Journal of the Korean Society for information Management
• /
• v.23 no.1 s.59
• /
• pp.201-219
• /
• 2006

This experimental study suggests an element-based XML document retrieval method that reveals highly relevant elements. The models investigated here for comparison are divergence and smoothing method, and hierarchical language model. In conclusion, the hierarchical language model proved to be most effective in element-based XML document retrieval with regard to the improved exhaustivity and harmed specificity.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.33
• /
• pp.29-37
• /
• 1995

An Automated Storage/Retrieval Systems(AS/RS) has been gradually emphasized because of the change of production and distribution environment. This paper develops algorithm and Shortest Distance Model that can reduce the traveling time of a stacker crane for efficient operation of AS/RS. In order to reduce the traveling time of a stacker crane, we determine the order processing and then the sequencing of storage/retrieval for each item. Order processing is determined based on the SPT(Shortest Processing Time) concept considering a criterion of retrieval coordinate. The sequencing of storage/retrieval is determined based on the Shortest Distance Model by using a modified SPP(Shortest Path Problem) of network problem. A numerical example is provided to illustrate the developed algorithm and Shortest Distance Model.

• Journal of Korean Library and Information Science Society
• /
• v.36 no.2
• /
• pp.49-68
• /
• 2005

The purpose of this study is to examine the research trends and their experiment results on the applications of the language models for information retrieval. We reviewed the previous studies with the following categories: (1) the first generation of language modeling information retrieval (LMIR) experiments which are mainly focused on comparing the language modeling information retrieval with the traditional retrieval models in their retrieval performance, and (2) the second generation of LMIR experiments which are focused on comparing the expanded language modeling information retrieval with the basic language models in their retrieval performance. Through the analysis of the previous experiments results, we found that (1) language models are outperformed the probabilistic model or vector space model approaches, and (2) the expended language models demonstrated better results than the basic language models in their retrieval performance.

• Journal of the Korean Society for information Management
• /
• v.24 no.1 s.63
• /
• pp.209-226
• /
• 2007

XML documents format on the Web provides a mechanism to impose their content and logical structure information. Therefore, an XML processor provides access to their content and structure. The purpose of this study is to investigate the usefulness of structural hints in the leaf node language model-based XML document retrieval. In order to this purpose, this experiment tested the performances of the leaf node language model-based XML retrieval system to compare the queries for a topic containing only content-only constraints and both content constrains and structure constraints. A newly designed and implemented leaf node language model-based XML retrieval system was used. And we participated in the ad-hoc track of INEX 2005 and conducted an experiment using a large-scale XML test collection provided by INEX 2005.

• The KIPS Transactions:PartB
• /
• v.19B no.3
• /
• pp.183-188
• /
• 2012

One of the major challenge for retrieval performance is the word mismatch between user's queries and documents in information retrieval. To solve the word mismatch problem, we propose a retrieval model based on the degree of association of query concept and word translation probabilities in translation-based model. The word translation probabilities are calculated based on the set of a sentence and its succeeding sentence pair. To validate the proposed method, we experimented on TREC AP test collection. The experimental results show that the proposed model achieved significant improvement over the language model and outperformed translation-based language model.

• The Korean Journal of Applied Statistics
• /
• v.32 no.5
• /
• pp.763-782
• /
• 2019

This paper proposes a probabilistic document ranking model incorporating term dependencies. Document ranking is a fundamental information retrieval task. The task is to sort documents in a collection according to the relevance to the user query (Qin et al., Information Retrieval Journal, 13, 346-374, 2010). A probabilistic model is a model for computing the conditional probability of the relevance of each document given query. Most of the widely used models assume the term independence because it is challenging to compute the joint probabilities of multiple terms. Words in natural language texts are obviously highly correlated. In this paper, we assume a multinomial distribution model to calculate the relevance probability of a document by considering the dependency structure of words, and propose an information retrieval model to rank a document by estimating the probability with the maximum entropy method. The results of the ranking simulation experiment in various multinomial situations show better retrieval results than a model that assumes the independence of words. The results of document ranking experiments using real-world datasets LETOR OHSUMED also show better retrieval results.

• International Journal of CAD/CAM
• /
• v.6 no.1
• /
• pp.117-123
• /
• 2006

Recently with the development of 3D modeling and digitizing tools, more and more models have been created, which leads to the necessity of the technique of 3D mode retrieval system. In this paper we investigate a new method for 3D model retrieval based on orthogonal projections. We assume that 3D models are composed of trigonal meshes. Algorithms process first by a normalization step in which the 3D models are transformed into the canonical coordinates. Then each model is orthogonally projected onto six surfaces of the projected cube which contains it. A following step is feature extraction of the projected images which is done by Moment Invariants and Polar Radius Fourier Transform. The feature vector of each 3D model is composed of the features extracted from projected images with different weights. Our System validates that this means can distinguish 3D models effectively. Experiments show that our method performs quit well.

• Korean Journal of Biological Psychiatry
• /
• v.14 no.1
• /
• pp.61-67
• /
• 2007

Objectives : The purpose of this study was to test stage model in Traumatic Brain Injury(TBI) patients. According to the stage model, attention deficits which is basic stage in information processing lead to memory disturbance and subsequently affect higher-order cognitive function such as memory, decision-making, abstract thinking, and judgement related to executive function. Therefore, it was hypothesized that attention affect recall(retrieval efficacy) related to executive function mostly relative to other cognitive function, in TBI patients with low executive function. Methods : Participants were referred to a TBI clinic and then was rated on K-WAIS and Executive Intelligence Test(EXIT). Participants were divided into two groups according to Executive IQ(EIQ) score, which of high function group(N=67) was more than 80(above low average) and of low function group(N=52) was under 80 (under borderline). To test the stage model, using hierarchical regression analysis, recall(retrieval efficacy) was regressed on 3 subscales(attention, verbal, visuospatial scale) after controlling for IQ according to each group. Furthermore, the mediation effect of attention between retrieval efficacy and verbal, visuospatial score was analyzed. Results : In the low function group, only attention area predicted significantly recall(retrieval efficacy), indicating that lower attention were related to lower EIQ after controlling for IQ. In the high function group, no area predicted significantly retrieval efficacy. In the low function group, verbal and visuospatial scale did not predicted significantly retrieval efficacy, indicating that there was no evidences supporting the mediation model. Conclusion : Only attention affect retrieval efficacy in TBI patients with low executive function. But, the mediation effect of attention between retrieval efficacy and verbal and visuospatial scale was not tested in the low function group. These results implied that stage model was tested partially. In treating cognitive deficit in TBI patients, it is necessary to develop cognitive rehabilitation program based on stage model. Furthermore, it is necessary to necessary to test mediation model in the future study.