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Internets are important in everyone's life like searching keyword, college, social network and online shopping, when user using the internet for searching the keyword they getting some problem. That is when user searching for the keyword for some meaning but they will get different meaning for that keyword. Because
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
Traditional information gathering systems are mostly keyword-based that are lack of semantic comprehension and analysis ability and can't guarantee the comprehensiveness and accuracy of information gathering. This paper proposes Chinese patent information gathering model based on domain ontology, which can visualize
machines interacting with other machines to yield results which are user oriented and precise. A New Integrated Case And Relation Based Page Rank Algorithm have been proposed to rank the results of a search system based on a user's topic or query. This paper proposes an optimized semantic searching of keywords represent by
keywords. These Web pages are ranked by a newly introduced equation. It is evident that all matched Web pages are not selected by this keyword selection procedure. Hence, unmatched Web pages are checked and named as ‘Secondary’ Web pages. These Web pages are ranked by another new equation. Ranking procedure of
keywords, which lack the semantic data. In this paper the semantic based information retrieval methodology is proposed to get data from the web archives in a specific domain by gathering the domain relevant information with web crawler. By utilizing ontology and semantic information matched with a given user's query is used
similar product images on shopping websites, ranking product tags by text aggregation, and re-search textual items consisting of semantic meaningful tags to make a recommendation. In addition, users can choose automatically suggested keywords to reflect their intentions. Subjective evaluation has demonstrated the
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