• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.103-120
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• 2007

Node is the semantic unit and a part of structured document. Information retrieval from structured documents offers an opportunity to go subdivided below the document level in search of relevant information, making any element in an structured document a retrievable unit. The node-based document retrieval constitutes several similarity calculating methods and the extended node retrieval method using structure information. Retrieval performance is hardly influenced by the methods for determining document similarity The extended node method outperformed the others as a whole.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.39-55
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• 2013

It is a difficult problem to use keyword retrieval for low-quality Korean document images because these include adjacent characters that are connected. In addition, images that are created from various fonts are likely to be distorted during acquisition. In this paper, we propose and test a keyword retrieval system, using a support vector machine (SVM) for the retrieval of low-quality Korean document images. We propose a keyword retrieval method using an SVM to discriminate the similarity between two word images. We demonstrated that the proposed keyword retrieval method is more effective than the accumulated Optical Character Recognition (OCR)-based searching method. Moreover, using the SVM is better than Bayesian decision or artificial neural network for determining the similarity of two images.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1327-1339
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• 2023

World Wide Web (WWW) use has been increasing recently due to users needing more information. Lately, there has been a growing trend in the document information available to end users through the internet. The web's document search process is essential to find relevant documents for user queries.As the number of general web pages increases, it becomes increasingly challenging for users to find records that are appropriate to their interests. However, using existing Document Information Retrieval (DIR) approaches is time-consuming for large document collections. To alleviate the problem, this novel presents Spatial Clustering Ranking Pattern (SCRP) based Density Ant Colony Information Retrieval (DACIR) for user queries based DIR. The proposed first stage is the Term Frequency Weight (TFW) technique to identify the query weightage-based frequency. Based on the weight score, they are grouped and ranked using the proposed Spatial Clustering Ranking Pattern (SCRP) technique. Finally, based on ranking, select the most relevant information retrieves the document using DACIR algorithm.The proposed method outperforms traditional information retrieval methods regarding the quality of returned objects while performing significantly better in run time.

• The Journal of Society for e-Business Studies
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• v.7 no.2
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• pp.21-38
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• 2002

Document retrieval (DR) has been a serious issue for long in the field of Office Information Management. Nowadays, our daily work is becoming heavily dependent on the usage of information collected from the internet, and the DR methods on the Web has become an important issue which is studied more than any other topic by many researchers. The main purpose of this study is to develop a model to manage business documents by integrating three major methodologies used in the field of electronic library and information retrieval: Metadata, Thesaurus, and Index/Reversed Index. In addition, we have added a new concept of eDocument, which consists of metadata about unit documents and/or unit document themselves. eDocument is introduced as a way to utilize existing document sources. The core concepts and structures of the model were introduced, and the architecture of the eDocument management system has been proposed. Test (simulation) result of the model and the direction for the future studies were also mentioned.

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

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.27-32
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• 2004

A XML Document has a structure which may be irregular The irregular document structure is difficult for users to know exactly. In this paper, we propose the XML document model and the structure retrieval method for efficient management and structure retrieval of XML documents. So we use fixed-sized LETID having the information of element, describe the structured information retrieval algorithm for parent and child element to represent the structured information of XML documents. Using this method, we represent the structured information of XML document efficiently. We can directly access to specific clement by simple operation, and process various queries. We expect the method to support various structured retrieval of specific element such as parent, child. and sibling elements.

• Journal of the Korean Society for Library and Information Science
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• v.6
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• pp.87-103
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• 1979

Information storage and retrieval is a part of general communication process. In the Shannon's information theory, information contained in a message is a measure of -uncertainty about information source and the amount of information is measured by entropy. Indexing is a process of reducing entropy of information source since document collection is divided into many smaller groups according to the subjects documents deal with. Significant concepts contained in every document are mapped into the set of all sets of index terms. Thus index itself is formed by paired sets of index terms and documents. Without indexing the entropy of document collection consisting of N documents is $log_2\;N$, whereas the average entropy of smaller groups $(W_1,\;W_2,...W_m)$ is as small $(as\;(\sum\limits^m_{i=1}\;H(W_i))/m$. Retrieval efficiency is a measure of information system's performance, which is largely affected by goodness of index. If all and only documents evaluated relevant to user's query can be retrieved, the information system is said $100\%$ efficient. Document file W may be potentially classified into two sets of relevant documents and non-relevant documents to a specific query. After retrieval, the document file W' is reclassified into four sets of relevant-retrieved, relevant-not retrieved, non-relevant-retrieved and non-relevant-not retrieved. It is shown in the paper that the difference in two entropies of document file Wand document file W' is a proper measure of retrieval efficiency.

• MALSORI
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• no.62
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• pp.133-147
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• 2007

In this paper, we introduce a phone vector discrete HMM(PVDHMM) that decodes a phone sequence string, and demonstrates the applicability to spoken document retrieval. The PVDHMM treats a phone recognizer or large vocabulary continuous speech recognizer (LVCSR) as a vector quantizer whose codebook size is equal to the size of its phone set. We apply the PVDHMM to decode the phone sequence strings and compare the outputs with those of a continuous speech recognizer(CSR). Also we carry out spoken document retrieval experiment through PVDHMM word spotter on the phone sequence strings which are generated by phone recognizer or LVCSR and compare its results with those of retrieval through the phone-based vector space model.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.14 no.1
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• pp.219-230
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• 2003

The purpose of this study is to improve of retrieval effectiveness to clustered and filtered document through query expansion. The result of this research prove that extended queries and documents, information in encyclopedia, clustering and filtering techniques are effective to promote retrieval effectiveness.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.663-667
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• 2010

A simple approach to semantic document-retrieval is to measure document similarity based on the bag-of-words representation, e.g., cosine similarity between two document vectors. However, such a syntactic method hardly considers the semantic similarity between documents, often producing semantically-unsound search results. We circumvent such a problem by combining supervised machine learning techniques with ontology information based on Markov logic. Specifically, Markov logic networks are learned from similarity-tagged documents with an ontology representing the diverse relationship among words. The learned Markov logic networks, the ontology, and the training documents are applied to the semantic document-retrieval task by inferring similarities between a query document and the training documents. Through experimental evaluation on real world question-answering data, the proposed method has been shown to outperform the simple cosine similarity-based approach in terms of retrieval accuracy.