• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.69-76
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• 2012

This paper proposes a novel way of personalized web search through re-ranking the search results with user profiles of concept-network structure. Basically, personalized search systems need to be based on user profiles that contain users' search patterns, and they actively use the user profiles in order to expand initial queries or to re-rank the search results. The proposed method is a sort of a re-ranking personalized search method integrated with query expansion facility. The method identifies some documents which occur commonly among a set of different search results from the expanded queries, and re-ranks the search results by the degree of co-occurring. We show that the proposed method outperforms the conventional ones by performing the empirical web search with a number of actual users who have diverse information needs and query intents.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.511-537
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• 2013

Numerous data intensive applications demand the efficient processing of a new type of query, which is called a progressive query (PQ). A PQ consists of a set of unpredictable but inter-related step-queries (SQ) that are specified by its user in a sequence of steps. A conventional DBMS was not designed to efficiently process such PQs. In our earlier work, we introduced a materialized view based approach for efficiently processing PQs, where the focus was on selecting promising views for materialization. The problem of how to efficiently find usable views from the materialized set in order to answer the SQs for a PQ remains open. In this paper, we present a new index technique, called the Dynamic Materialized View Index (DMVI), to rapidly discover usable views for answering a given SQ. The structure of the proposed index is a special ordered tree where the SQ domain tables are used as search keys and some bitmaps are kept at the leaf nodes for refined filtering. A two-level priority rule is adopted to order domain tables in the tree, which facilitates the efficient maintenance of the tree by taking into account the dynamic characteristics of various types of materialized views for PQs. The bitmap encoding methods and the strategies/algorithms to construct, search, and maintain the DMVI are suggested. The extensive experimental results demonstrate that our index technique is quite promising in improving the performance of the materialized view based query processing approach for PQs.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.354-360
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• 2006

The purpose of this paper is to propose a securely personalized context-aware cooperative query that supports a multi-level data abstraction hierarchy and conceptual distance metric among data values, while considering privacy concerns around user context awareness. The conceptual distance expresses a semantic similarity among data values with a quantitative measure, and thus the conceptual distance enables query results to be ranked. To show the feasibility of the methodology proposed in this paper we have implemented a prototype system in the area of site search in a large-scale shopping mall.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.611-616
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• 2017

In the area of Information Retrieval, Query Expansion is a well-known technique that uses external knowledge to increase an inquiry generated by users. However, ambiguous words used in the query decrease the performance of search tools. In this paper, we propose a solution to the above problem, by using domain knowledge which identifies the meaning of words in the query. In particular, we present a domain centered query expansion technique that magnifies a query using domains. By comparing with various query expansion models, we demonstrate that the proposed model performs better than the other models.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.85-92
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• 2005

Recently, query processing techniques for the multi-dimensional data like images have been widely used to perform content-based retrieval of the data . Range query and Nearest neighbor query are widely used multi dimensional queries . This paper Proposes the efficient pruning strategies for k-nearest neighbor query in R-tree variants indexing structures. Pruning strategy is important for the multi-dimensional indexing query processing so that search space can be reduced. We analyzed the Pruning strategies and perform experiments to show overhead and the profit of the strategies. Finally, we propose best use of the strategies.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.23-48
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• 2013

This study compared and analyzed 15 CBMR (Content-based Music Retrieval) systems accessible on the web in terms of DB size and type, query type, access point, input and output type, and search functions, with reviewing features of music information and techniques used for transforming or transcribing of music sources, extracting and segmenting melodies, extracting and indexing features of music, and matching algorithms for CBMR systems. Application of text information retrieval techniques such as inverted indexing, N-gram indexing, Boolean search, truncation, keyword and phrase search, normalization, filtering, browsing, exact matching, similarity measure using edit distance, sorting, etc. to enhancing the CBMR; effort for increasing DB size and usability; and problems in extracting melodies, deleting stop notes in queries, and using solfege as pitch information were found as the results of analysis.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.687-694
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• 2003

According to the rapid increase of multimedia data quantity recently, various means of video data search has been desired. In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we design the system that can heighten retrieval efficiency of video data through semantic-based retrieval.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3102-3119
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• 2021

In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.335-342
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• 2018

The search service, which is a core service of the portal site, presents search queries that are rapidly increasing among the inputted search queries based on the highest instantaneous search frequency, so it is difficult to immediately notify a search query having a high degree of interest for a certain period. Therefore, it is necessary to overcome the above problems and to provide more intelligent information retrieval service by bringing improved analysis results on the change of the search queries. In this paper, we present the criteria for measuring the interest, continuity, and attention of real-time search queries. In addition, according to the criteria, we measure and summarize changes in real-time search queries in hours, days, weeks, and months over a period of time to assess the issues that are of high interest, long-lasting issues of interest, and issues that need attention in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.414-422
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• 2011

IR (Information Retrieval) systems have the methods that compare relationships between query and index to identify document that may be fit to the user's query keyword. However, the methods usually ignore the importance of relations that are not expressed in the query. Therefore, in this study, we describe how to refine the queries' relation from keyword and to reveal the hidden intent. A useful relationship between query and keyword in IR wth studied and we classified the tion fromrelation. Firstfromall, we did researchmrelated on semantic relationship and ontolhiical researchmin foreign and domestic research, and also analyzed semantic network practices, information retrieval technolhiy, extracted and classified the tion fromrelationships s' relasite's real-world datamin whichminformation retrieval technolhiin fare applied. Next, we souiht to solve the problems occurred frequently i' relasituation that searchers tioically face. I' relacurrent search technolhiy, the mesh searchmresult fare poured by simply comparn ina query with index terms. Therefore, the need for an intelligent search fittn inusers' intent is required. The relationships between two queries to re hiddee and identify relasearcher's intent have to be revealed. By analyzn inthe practical cthes s' queries and classifyn inthem into nine kind fromrelationship tion, we proposed the method to design relation revealn inand role namn i, and we have also illustrated limitations of that methods.