• Annual Conference on Human and Language Technology
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• 1999.10d
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• pp.6-8
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• 1999

• Journal of Information Science Theory and Practice
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• v.8 no.3
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• pp.30-46
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• 2020

This research aimed (1) to study and analyze the ability of current information retrieval (IR) systems based on views of information behavior (IB), and (2) to propose a conceptual framework for an IB model based on the collaboration between the system and user, with the intent of developing an IR system that can apply intelligent techniques to enhance system efficiency. The methods in this study consisted of (1) document analysis which included studying the characteristics and efficiencies of the current IR systems and studying the IB models in the digital environment, and (2) implementation of the Delphi technique through an indepth interview method with experts. The research results were presented in three main parts. First, the IB model was categorized into eight stages, different from traditional IB, in the digital environment, which can correspond to all behaviors and be applied to with an IR system. Second, insufficient functions and log file storage hinder the system from effectively understanding and accommodating user behavior in the digital environment. Last, the proposed conceptual framework illustrated that there are stages that can add intelligent techniques to the IR system based on the collaboration perspective between the user and system to boost the users' cognitive ability and make the IR system more user-friendly. Importantly, the conceptual framework for the IB model based on the collaboration perspective between the user and system for IR assisted the ability of information systems to learn, recognize, and comprehend human IB according to individual characteristics, leading to enhancement of interaction between the system and users.

• Journal of the Korean Society for information Management
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• v.26 no.2
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• pp.173-194
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• 2009

Recently, semantic search techniques which are based on information space as consisting of nonambiguous, non-redundant, formal pieces of ontological knowledge have been developed so that users do exploit large knowledge bases. The purpose of the study is to design more user-friendly and smarter retrieval interface based on ontological analysis, which can provide more precise information by reducing semantic ambiguity or more rich linked information based on well-defined relationships. Therefore, this study, first of all, focuses on ontological analysis on researcher information as selecting descriptive elements, defining classes and properties of descriptive elements, and identifying relationships between the properties and their restriction between relationships. Next, the study designs the prototypical retrieval interface based on ontology-based representation, which supports to semantic searching and browsing regarding researchers as a full-fledged domain. On the proposed retrieval interface, users can search various facts for researcher information such as research outputs or the personal information, or carrier history and browse the social connection of the researchers such as researcher group that is lecturing or researching on the same subject or involving in the same intellectual communication.

• Journal of Information Science Theory and Practice
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• v.7 no.1
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• pp.29-38
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• 2019

Information retrieval is the challenge of the Web 2.0 world. The experiment of knowledge organisation in the context of abundant information available from various sources proves a major hurdle in obtaining information retrieval with greater precision and recall. The fast-changing landscape of information organisation through social networking sites at a personal level creates a world of opportunities for data scientists and also library professionals to assimilate the social data with expert created data. Thus, folksonomies or social tags play a vital role in information organisation and retrieval. The comparison of these user-created tags with expert-created index terms, author keywords and title words, will throw light on the differentiation between these sets of data. Such comparative studies show revelation of a new set of terms to enhance subject access and reflect the extent of similarity between user-generated tags and other set of terms. The CiteULike tags extracted from 5,150 scholarly journal articles in marine science were compared with corresponding Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title terms. The Jaccard similarity coefficient method was employed to compare the social tags with the above mentioned wordsets, and results proved the presence of user-generated keywords in Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title words. While using information retrieval techniques like stemmer and lemmatization, the results were found to enhance keywords to subject access.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1150-1166
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• 2018

This paper presents a physical storage design method for image access structures using transformation techniques of multidimensional file organizations in image information systems. Physical storage design is the process of determining the access structures to provide optimal query processing performance for a given set of queries. So far, there has been no such attempt in the image information system. We first show that the number of pages to be accessed decreases as the shape of the given retrieval query region and that of the data page region become similar in the transformed domain space. Using these properties, we propose a method for finding an optimal image access structure by controlling the shapes of the page regions. For the performance evaluation, we have performed many experiments with a multidimensional file organization using transformation techniques. The results indicate that our proposed method is at least one to maximum five times faster than the conventional method according to the query pattern within the scope of the experiments. The result confirms that the proposed physical storage design method is useful in a practical way.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.287-295
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• 2007

This paper proposes a novel data editing techniques with genetic algorithm (GA) in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in compelax problem solving. Nonetheless, compared to other machine teaming techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However. designing a good matching and retrieval mechanism for CBR system is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for data editing in CBR.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.1-17
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• 2021

Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.621-628
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• 2000

A retrieval performance of VOD sever is estimated by how quickly it services popular videos to users and how many users it is able to service. Each video data is placed on heterogeneous disks and placement techniques are various, retrieval performance is under the control of these elements, so that a retrieval performance is affected by placement policy. In this paper, we place video data considering their characteristics, especially, we place videos distributively according to their popularity. To verify our policy, we make various environment of experiment, estimate a placement policy using popularity of videos and a contrary policy, and compare them.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.35-38
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• 2004

기존의 내용기반 이미지 검색 시스템은 색상, 질감, 모양등과 같은 특징 벡터를 추출하여 검색하는 방법이 많이 연구되어 왔다. 특히 색상 정보는 이미지를 검색하기 위하여 중요한 정보로 사용되고 있다. 따라서 색상 이미지를 검색하기 위해서 평균 RGB, HSI값을 이용하거나 히스토그램을 이용하는 방식이 많이 사용 되어왔다. 본 논문에서는 사람이 시각적으로 보고 느끼는 색상(H), 채도(S), 명도(I) 방식을 이용한 HSI값을 사용하여 색상 의미를 이용한 지능적 이미지 검색 기법을 제안하고 알고리즘을 설명한다. 색상 의미(Color Semantics)란 사람의 시각적인 특징을 기반으로 칼라 이미지에 적용하여 감성 형용사 기반으로 검색할 수 있는 방법이다. 색상 의미를 이용한 지능적 이미지 검색은 색상-기반 질의(color-based retrieval)를 제공할 뿐만 아니라 인간의 감성이나 느낌에 의한 의미-기반 질의(semantic-based retrieval)방식을 가능하게 한다. 즉, "시원한 이미지" 혹은 "부드러운 이미지"를 검색하는 방식이다. 따라서 사용자의 검색 의도를 보다 정확하게 표현할 수 있으며, 검색의 결과에 대한 만족도를 향상 시킬 수 있다.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.209-223
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• 2000

As the importance and utilization of multimedia data increases, it becomes necessary to represent and manage multimedia data within database systems. In this paper, we designed and implemented an image retrieval system which support efficient management and intelligent retrieval of image data using concept hierarchy and data mining techniques. We stored the image information intelligently in databases using concept hierarchy. To support intelligent retrievals and efficient web services, our system automatically extracts and stores the user information, the user's query information, and the feature data of images. The proposed system integrates user metadata and image metadata to support various retrieval methods on image data.