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semantic web search engines relates user keyword with terms, entities, texts, documents which have semantic correlation with user query. Both search engines does not use images within web pages to find more relevant information. Now in this paper we have formulated a web document integrated ranking method based on text
many fields like artificial intelligence, cognitive science, natural language processing, psychology. To relate entities/texts/documents having same meaning, semantic similarity approach is used based on matching of the keywords which are extracted from the documents using syntactic parsing. The simple lexical matching
order to collect and index only related Web documents. As requests can be insufficient to express sensitive and specific needs, the user's information needs are specified using user's interests represented by DBPedia resources [1] and keywords, both extracted from Web pages provided by the user. A series of experiments
Recently the numbers of Arabic web sites are rapidly increasing in the World Wide Web. Existing search engines retrieve information based on keywords, so huge number of irrelevant information is retrieved for users. By the appearing of the second generation of the World Wide Web the semantic web, many suitable
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.