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information. Keyword based information retrieval technique helps in improving recall of user query result, but having low precision. To improve precision, we adopt semantic information retrieval technique. We are proposing architecture for semantic based information retrieval, in which plain text is read semantically and the
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
Current search engine performances need to be improved because often the result suggested by search engine are determine the popularity of a given page for its associated keywords but does not match specific user expectations. Previous researches have indicated that only 20% to 45% of the common search results are
This paper proposes a novel personalized news recommendation system named InfoSlim. The new system uses semantic technique to annotate news items and user preference in order to add rich metadata information into traditional keyword vector. By doing this, the similarity measure between item profile and user profile
keywords that are usually used to describe that topic or category. Additional keywords that the user frequently associates with a topic, such as names of important people, organizations, or a specialized terminology, etc. Are also incorporated into the relevant topic. We use the Apriori Algorithm to extract these associated
creative ideas to customers. In order to solve this problem, this paper presents algorithms to achieve customers' target. This project can be divided into three parts. The first part is to enrich and to analyse the input keywords by semantic web. The second part is to general raw ideas and relevant ideas by an inference
Current techniques for retrieving content and usage information from educational data are based on keywords which including string combinations. This technique raises the limitation in terms of capturing learning conceptualization associated to the results. Aims to reveal this issue, this paper present an approach of
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