• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.63-73
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• 2011

In general, conventional search engines provide the same search results for the same queries of users, and however such techniques do not consider users' characteristics. To overcome this problem, we need a new way of personalized search which returns customized search results according to users' preference. In this paper, we propose a concept network profile-based personalized web search system in which the concept network is developed for accumulating users' characteristics. The concept network-based user profile is used to expand initial search queries to achieve personalized search. The concept network is a network structure of concepts where each concept is generated whenever each query is submitted, and it can be defined as a set of keywords extracted from the selected documents. Furthermore, we have improved the concept networks by augmenting intent keywords of each concept with a set of classification tags, called folksonomy, assigned to each document. For an additional personalized search technique, we propose a new re-ranking method that analayzes the degree of overlapped search results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1373-1387
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• 2012

Contextual advertising is an important revenue source for major service providers on the Web. Ads classification is one of main tasks in contextual advertising, and it is used to retrieve semantically relevant ads with respect to the content of web pages. However, it is difficult for traditional text classification methods to achieve satisfactory performance in ads classification due to scarce term features in ads. In this paper, we propose a novel ads classification method that handles the lack of term features for classifying ads with short text. The proposed method utilizes a vocabulary expansion technique using semantic associations among terms learned from large-scale search query logs. The evaluation results show that our methodology achieves 4.0% ~ 9.7% improvements in terms of the hierarchical f-measure over the baseline classifiers without vocabulary expansion.

• ETRI Journal
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• v.21 no.1
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• pp.40-54
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• 1999

In this paper, we describe a new trademark retrieval system based upon the content or the shape of trademark. The system has an on-line graphical user interface for the World Wide Web (WWW) that allows user to provide a query in forms of a sketch or a visual image to search for similar trademarks from database. User interfaces for the WWW were implemented by utilizing HTML and Java applets. The query can occur in arbitrary size and orientation. A shape representation scheme invariant to scale and rotation was developed to measure the similarity between two trademarks using the magnitude of Zernike moments as a feature set. Performance evaluation has been carried out with a database of 3,000 trademarks. It takes only about 0.6 second for the retrieval on a 200 MHz Pentium PC. The average recall of the original one among top 30 candidates queried by noisy or deformed images was 100%.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.562-566
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• 2009

In this paper, we present a novel system to categorize web image search results using emotional concepts and to browse the results more conveniently and easily. The proposed system can categorize search results into 8 emotional categories based on emotion vector, which obtained by color and pattern features. Here, we use Kobayashi’s emotional categories: {romantic, natural, casual, elegant, chic, classic, dandy and modern}. With search results for a given query, the proposed system can provide categorized images for each emotional category. With 1,000 Yahoo! search images, we compared the proposed method with Yahoo! image search engine in respect of satisfaction, efficiency, convenience and relevance with a user study. Our experimental results show the effectiveness of the proposed method.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.333-344
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• 2008

In recent years, the web services composition has received much attention. By web services composition, we mean providing a new service that does not exist on the repository. In this paper, we propose a new system called PSR for web services composition search using a relational database. We also propose algorithms for pre-computing web services composition using joins and indices. We store ontologies from web services in RDBMS, so that the PSR system returns web services composition in order of similarity with user query through the degree of the ontology matching. We demonstrated that our pre-computing web services composition approach in RDBMS yields lower execution time and good scalability when handling a large number of web services and user queries.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.202-214
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• 2005

The semantic web is the web paradigm that represents not general link of documents but semantics and relation of document. In addition it enables software agents to understand semantics of documents. We propose a semantic search based on inference with ontologies, which has the following characteristics. First, our search engine enables retrieval using explicit ontologies to reason though a search keyword is different from that of documents. Second, although the concept of two ontologies does not match exactly, can be found out similar results from a rule based translator and ontological reasoning. Third, our approach enables search engine to increase accuracy and precision by using explicit ontologies to reason about meanings of documents rather than guessing meanings of documents just by keyword. Fourth, domain ontology enables users to use more detailed queries based on ontology-based automated query generator that has search area and accuracy similar to NLP. Fifth, it enables agents to do automated search not only documents with keyword but also user-preferable information and knowledge from ontologies. It can perform search more accurately than current retrieval systems which use query to databases or keyword matching. We demonstrate our system, which use ontologies and inference based on explicit ontologies, can perform better than keyword matching approach .

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.357-365
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• 2010

Personalized web search engine provides personalized results to users by query expansion, re-ranking or other methods representing user's intention. The personalized result page includes URL, page title and small text fragment of each web document. which is known as snippet. The snippet is the summary of the document which includes the keywords issued by either user or search engine itself. Users can verify the relevancy of the whole document using only the snippet, easily. The document summary (snippet) is an important information which makes users determine whether or not to click the link to the whole document. Hence, if a search engine generates personalized document summaries, it can provide a more satisfactory search results to users. In this paper, we propose a personalized document summary system for personalized web search engines. The proposed system provides increased degree of satisfaction to users with marginal overhead.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.83-88
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• 2016

Subtopic mining is the extraction and ranking of possible subtopics, which disambiguate and specify the search intentions of an input query in terms of relevance, popularity, and diversity. This paper describes the limitations of previous studies on the utilization of web resources, and proposes a subtopic mining method with a two-level hierarchy based on hierarchical search intentions and web resources, in order to overcome these limitations. Considering the characteristics of resources provided by the official subtopic mining task, we extract various second-level subtopics reflecting hierarchical search intentions from web documents, and expand and re-rank them using other provided resources. Terms in subtopics with wider search intentions are used to generate first-level subtopics. Our method performed better than state-of-the-art methods in almost every aspect.

• Progress in Medical Physics
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• v.14 no.1
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• pp.43-50
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• 2003

PACS mostly has been used in large scaled hospital due to expensive initial cost to set up the system. The network of PACS is independent of the others: network. The user's PC has to be connected physically to the network of PACS as well as the image viewer has to be installed. The web based mini-PACS can store, manage and search inexpensively a large quantity of radiologic image acquired in a hospital. The certificated user can search and diagnose the radiologic image using web browser anywhere Internet connected. The implemented Image viewer is a viewer to diagnose the radiologic image. Which support the DICOM standard and was implemented to use JAVA programming technology. The JAVA program language is cross-platform which makes easier upgrade the system than others. The image filter was added to the viewer so as to diagnose the radiologic image in detail. In order to access to the database, the user activates his web browser to specify the URL of the web based PACS. Thus, The invoked PERL script generates an HTML file, which displays a query form with two fields: Patient name and Patient ID. The user fills out the form and submits his request via the PERL script that enters the search into the relational database to determine the patient who is corresponding to the input criteria. The user selects a patient and obtains a display list of the patient's personal study and images.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.41-56
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• 2005

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance. we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance.