• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.683-690
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• 2009

Social Networks which is composed of network with an individual in the center in a web support mutual-understanding of information by searching user profile and forming new link. Therefore, if we apply the Social Network which consists of web users who have similar immanent information to web search, we can improve efficiency of web search and satisfaction of web user about search results. In this paper, first, we make a Social Network using web users linked directly or indirectly. Next, we calculate Similarity among web users using their immanent information according to topics, and then reconstruct Social Network based on varying Similarity according to topics. Last, we compare Similarity with Search Pattern. As a result of this test, we can confirm a result that among users who have high relationship index, that is, who have strong link strength according to personal attributes have similar search pattern. If such fact is applied to search algorithm, it can be possible to improve search efficiency and reliability in personalized and social search.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.711-716
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• 2014

With the gradual increase of interest in plagiarism and intelligent file content search, the demand for similarity measuring between two sentences is increasing. There is a lot of researches for sentence similarity measurement methods in various directions such as n-gram, edit-distance and LSA. However, these methods have their own advantages and disadvantages. In this paper, we propose a new sentence similarity measurement method approaching from another direction. The proposed method uses the set-based POI data search that improves search performance compared to the existing hard matching method when data includes the inverse, omission, insertion and revision of characters. Using this method, we are able to measure the similarity between two sentences more accurately and more quickly. We modified the data loading and text search algorithm of the set-based POI data search. We also added a word operation algorithm and a similarity measure between two sentences expressed as a percentage. From the experimental results, we observe that our sentence similarity measurement method shows better performance than n-gram and the set-based POI data search.

• The Research Journal of the Costume Culture
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• v.22 no.5
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• pp.743-759
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• 2014

The purpose of this study is to identify the antecedents of consumers' decision postponement on purchasing fast fashion brands. Ongoing search behavior, overchoice confusion, and similarity confusion were considered as antecedents. It was hypothesized that ongoing search behavior influences decision postponement both directly and indirectly through overchoice confusion and similarity confusion. Data were gathered by surveying university students in Seoul, using convenience sampling. Three hundred five questionnaires were used in the statistical analysis, which were exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using AMOS. Factor analysis proved that ongoing search behavior, overchoice confusion, similarity confusion, and decision postponement were uni-dimensions. Tests of the hypothesized path proved that ongoing search behavior influences decision postponement indirectly through overchoice confusion. In addition, similarity confusion influences decision postponement. The results suggest some confusion reduction strategies for marketers of fast fashion brands. Suggestions for future study are also discussed.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.727-732
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• 2007

Most of previous work on indexing and searching time series focused on the similarity matching and retrieval of one-attribute time series. However, multimedia databases such as music, video need to handle the similarity search in multi-attribute time series. The limitation of the current similarity models for multi-attribute sequences is that there is no consideration for attributes' sequences. The multi-attribute sequences are composed of several attributes' sequences. Since the users may want to find the similar patterns considering attributes's sequences, it is more appropriate to consider the similarity between two multi-attribute sequences in the viewpoint of attributes' sequences. In this paper, we propose the similarity search method based on attributes's sequences in multi-attribute time series databases. The proposed method can efficiently reduce the search space and guarantees no false dismissals. In addition, we give preliminary experimental results to show the effectiveness of the proposed method.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.366-373
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• 2009

In this paper, we propose a similarity search algorithm for image databases. One of the central problems regarding content-based image retrieval (CBIR) is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms used in CBIR have used the Minkowski metric (or $L_p$-norm) to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information. Our new search algorithm tackles this problem by employing new similarity measures and ranking strategies that reflect the nonlinearity of human perception and contextual information. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.

• Journal of IKEEE
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• v.22 no.2
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• pp.491-494
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• 2018

This paper presents a similarity search method based on mixed norm for enhancing self-example-based single image super-resolution. In order to incorporate the local statistical characteristics of the patches into the super-resolution image reconstruction, we propose a method to determine the order of the norm according to the patch inclination and use it as a similarity search between patches. Experimental results demonstrate that the proposed similarity search method has the capability to improve the performance of existing search method.

• Journal of KIISE
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• v.43 no.11
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• pp.1275-1280
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• 2016

Existing techniques for string similarity search first generate a set of candidate strings and then verify the candidates. The efficiency of string similarity search is highly dependent on candidate generation methods. State of the art techniques select fixed length q-grams from a query string and generate candidates using inverted lists of the selected q-grams. In this paper, we propose a technique to generate candidates using variable length grams of a query string and develop a dynamic programming algorithm that selects an optimal combination of variable length grams from a query string. Experimental results show that the proposed technique improves the performance of string similarity search compared with the existing techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2485-2487
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• 2015

Recently, Hashtag is widely used in SNS like Facebook, Twitter and personal blogs. However, the efficiency of tag search system is poor due to the indiscriminate use of hashtags. To enhance the accuracy of tag search system, we proposed a tag search system using the keyword extraction and similarity evaluation. The experimental results show that the proposed system provides the higher accuracy on tag search results.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.317-324
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• 2012

Recently, coordination task of similarity between national R&D programs is emphasized on view from the R&D investment efficiency. But the previous similarity search method like text-based similarity search which using keyword of R&D projects has reached the limit due to deviation of document's quality. For the solve the limitations of text-based similarity search using the keyword extraction, in this study, utilization of R&D project's technical classification will be discussed as a new similarity search method when analyzed of similarity between national R&D programs. To this end, extracts the Science and Technology Standard Classification of R & D projects which are collected when national R&D Survey & analysis, and creates peculiar vector model of each R&D programs. Verify a reliability of this study by calculate the cosine-based and Euclidean distance-based similarity and compare with calculated the text-based similarity.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.5 s.182
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• pp.30-36
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• 2006

The participants in collaborative environment want to get the right information or documents which are intended to find. In general search systems, documents which contain only the keywords are retrieved. For searching different word-expressions for the same meaning, we perform mapping before searching. Our mapping-based search approach has two parts, ontology-based mapping logic and ontology libraries. The ontology-based mapping consists of three steps such as character matching (CM), definition comparing (DC) and similarity checking (SC). First, the character matching is the mapping of two terminologies that have identical character strings. Second, the definition comparing is the method that compares two terminologies' ontological definitions. Third, the similarity checking pairs two terminologies which were not mapped by two prior steps through evaluating the similarity of the ontological definitions. For the ontology libraries, document ontology library (DOL), keyword ontology library (KOL), and mapping result library (MRL) are defined. With these three libraries and three mapping steps, an ontology-based search engine (OntSE) is built, and a use case scenario is discussed to show the applicability.