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Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which
search. In this paper, we propose a framework for semantic based information retrieval. Here we find the concepts that user specify in their query by analyzing the semantic equivalencies. The result which is a set of alternate queries to the main search query is then compared with the existing keyword based system's result
shortlist the results. These popular Web search engines use first generation search service based on ??static keywords??, which require the users to type in the exact keywords. This approach clearly puts the users in a critical situation of guessing the exact keyword. The users may want to define their search by using
In this paper, an intelligent concept based search engine has been presented that can be used as a multilingual platform for different search queries. It retrieves those results pages also which don't have directly the keywords but contains the synonyms or related words. In response to a query for the word “car
A Max-Probability Density based Clustering (MPDC) algorithm is proposed in this paper to resolve the problem of Word Sense Disambiguation in semantic document. MPDC take the context information of a keyword based on WordNet into account and select the max probability sense by measuring the density of the concept. We
The existing search engines retrieve information only based on the keywords. The incapability to search on the basis of the relation between the keywords and the user concepts, generates noise and hence, results in irrelevant retrieval. This leads to the idea of performing Semantic information processing by mapping
The inability of the present search engines to map the retrieved result set using semantics of the query keywords has been discussed. The present study suggests a framework to improve the mapping of Concept and Context of the query keywords and thereby remove noise from the query. This ensures more relevant and
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