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Web pages for search engine. First we describe a scheme based on semantic keywords combined with sentence overlapping, and then show an implemented prototype, with the experimental results that suggest the prototype work well under a proper setting.
Due to the rapid growth of Web pages available on the Internet recently, searching a relevant and up-to-date information has become a crucial issue. Conventional search engines use heuristics to determine which Web pages are the best match for a given keyword. Results are obtained from a database that is located at
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
The topic correlation judgment algorithm based on weight and threshold is proposed as for the problem that Web pages which are closely related to the given topic may be neglected due to not all keywords given by the users in the pages when users retrieve the topic they desire on the Internet. The algorithm retrieves
extract the keywords in each document. The paper establishes the transformation between the keywords in documents and the binary granules, and adopts the algorithm of association rules based on granular computing to obtain frequent item sets between documents. Bring in the set theory thought, numbers of the same word between
Social bookmarking and other Web sites allow users submitting their resources and labeling them with arbitrary keywords, called tags, to create folksonomies. These sites usually provide their users tag recommendations in order to help them to find relevant information and resources. However, only very basic techniques
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
-demand into the sentence importance. The user-demand consists of the keywords that user queried. The experimental results show that this method can improve the accuracy of searching information.
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