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multilingual information where backend will be English database and front-end uses local languages like Hindi, Marathi or Gujrathi. Our system provides an interface to enter a keyword in local language, the keyword will be parsed, query will be formed and display the result in local language. We had developed an efficient
-processing of Web search results have been extensively studied to help user effectively obtain useful information. This paper has basically three parts. First part is the review study on how the keyword is expanded through truncation or wildcards (which is a little known feature but one of the most powerful one) by using
In this paper we focus on building a large scale keyword search service over structured peer-to-peer (P2P) networks. Current state-of-the-art keyword search approaches for structured P2P systems are based on inverted list intersection. However, the biggest challenge in those approaches is that when the indices are
This paper proposes a novel method to generate labels for grouping and organizing the search results returned by auxiliary search engines. It has applied statistical techniques to measure the quantities of co-occurrence keywords for forming the label matrix of them, and then agglomerated them into higher-level
The field of Information Retrieval plays an important role in searching on the Internet. Most of the information retrieval systems are limited to the query processing based on keywords. In information retrieval system the matching of the query against a set of text record is the core of the system. Retrieval of the
The explosive growth of content makes it difficult for end-users to find data that they want. Keyword-based searches are brittle - they require the user to know the set of keywords that the system is using, and are ineffective for finding data based on meaning. This paper describes a new hierarchical overlay model
The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has an exact goal in mind in searching a set of images whereas users
Web application parameter contains identical structure and value. As a result, parameter features repetition of identical variable name and keyword. The characteristic of the keywords can be represented by being extracted from parameter. In order to measure the identity between two sequences, genome alignment which
Keyword-based web search engine uses text to reflect users' query intentions. However, it is hard to descript user's intention with simple text terms accurately, and besides of this, it is also hard to make the association between the text terms and images precisely. As a result, the keyword-based image search engine
and completeness through sense disambiguation and contextual meta-data prepossessing. Our schemes exploits a linguistic ontology identifying query relevant homographs used to construct sense specific keyword sets allowing for enhanced image search and result ranking via the calculation of relatedness between query
The goal of this paper is to cross-lingually analyze multilingual blogs collected with a topic keyword. The framework of collecting multilingual blogs with a topic keyword is designed as the blog feed retrieval procedure. Multilingual queries for retrieving blog feeds are created from Wikipedia entries. Finally, we
sense discovery problem. Given a query and a list of result pages, our unsupervised method detects word sense communities in the extracted keyword network. The documents are assigned to several refined word sense communities to form clusters. We use the modularity score of the discovered keyword community structure to
This paper proposes a new re-ranking scheme and presents experimental performance results for Web image retrieval with integrated query. In our previous work, cross-modal association rule was designed for associating one keyword with several visual feature clusters in Web image retrieval. Based on the cross-modal
In this work, we compare various text-based pornographic Web filtering techniques. The techniques include blacklist and keyword blocking. The technique called SV is modified to extract a representative feature vector. Each test Web pagepsilas feature is extracted and gathered as a vector. The vector is then summarized
The amount of information on the Web is growing at an exponential rate. Information overload has brought a heavy burden for modern life. Keyword based search engines no long fill the needs of many people. This paper introduces an approach towards intelligent information retrieval by providing clustered Web pages and
based on keyword indexing, there are many records in their result lists that are irrelevant to the user's information needs. It is shown that for retrieving more relevant and precise results, the following two points should be concerned: First of all, the query (either it is generated by a human or an intelligent agent
the device to strengthen the defense. To enhance the security of the back-end application servers, we use keyword filtering and re-treatment to rule out the blacklist, and to adjust the system settings so that it can effectively block the assaults or reduce the possibility of successful attacks. In addition, we also
, interests and dislikes. If this data can be extracted and analyzed effectively, useful items, news or people can be recommended. There are high number of studies that extract keywords from texts in order to obtain such information, however, micro blogs have noisy text blocks, and hence regular text extraction algorithms fail
collaboration by stressing, warning, and presenting keywords/summaries in multimedia. Effects of presenting keywords/summaries adaptively depending on situations are evaluated as to the decrease of not-/misunderstanding possibilities during the explanation on the Cyberspace. Moreover, the adaptive selection effects of keywords or
integration. We propose a framework for DBMS-IRS integration that uses top ranked terms from a database query result as keywords for an IRS search, thus retrieving documents strongly related to the query. Indeed, the framework uses the ranked terms to “expand” an initial keyword search provided by the user
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