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With the Web of today being unstructured and semantically heterogeneous, keyword-based queries are likely to miss important results. Therefore, refining and expanding queries plays an important role today. This paper presents a novel approach for query expansion that applies dependency rules mined from a large Web
With the development of Internet, more and more on-line information has become precious wealth that we can access to. High quality information is often stored in dedicated digital libraries. However, query system of most digital libraries based on keyword matching couldnpsilat make users satisfied. This paper presents
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
This system proposes Indian-logic ontology based Context-aware Query Refinement model to support context-sensitive semantic search in keyword based search engine. This is by formulating effective query using Indian logic based Ontology for Context identification to overcome ambiguous query terms and increase the
FCA, a session interest concept is defined as a pair of extent and intent where the extent covers a set of documents selected by the user among the search results and the intent covers a set of keyword features extracted from the selected documents. And, in order to make a concept network grow, we need to calculate the
A simple search keyword usually returns million of search results. The result count may appear impressive, at the same time it confuse the users. User usually will not wish to browse through million of entries. This paper proposed a query refinement method by iterative clustering of information from the Web page
Most researches on Image Retrieval (IR) have aimed at clearing away noisy images and allowing users to search only acceptable images for a target object specified by its object-name. We have become able to get enough acceptable images of a target object just by submitting its object-name to a conventional keyword
Inherent ambiguity of short keyword queries demands for enhanced methods for Web retrieval. In this paper we propose to have twice query expansion, the first query expansion is progressed by determining the relatedness between two word senses via structural and domain relatedness computation based on WordNet and
. When the user inputs query keywords, more personalized expansion words are generated by the proposed algorithm, and then these words together with the query keywords are submitted to a popular search engine such as Baidu or Google. These expansion words can help a search engine retrieval information for a user according
Applying automatic summarization to search engine can make it easier for users to find out the content of the Web page. In this paper, the results of search engine are analyzed. On the basis of query keywords expansion, we propose a new summary approach which calculates the sentence weight utilizing the information of
In this paper, we examine the significance of expansion of the user query by two techniques namely Efficient Clustering-By-Direction and Theme Clustering. These two techniques produce the clusters of keywords extracted from the set of retrieved documents for the user query. The former clustering is based on
The temporal information is an essential attribute in the web page, such as the publish time and the content time in the web page. However, the major search engine does not have more view on the temporal information of web page, and ignored the relationship between the keywords and time phrases. In this paper, we
based on ontology. It uses the rich semantic knowledge of ontology to upgrade the retrieval based on keywords to concepts, and combines it with the specialized engine to improve retrieval effect and efficiency. The paper also takes patent information for example to explain its application at the end.
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