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A user who wants to get information from a relational database needs to know database schema and structured query languages like SQL. The ordinary users are not familiar to those things, so searching information from relational databases is hard to them. Keyword search is a solution of the problem, where a keyword
Given a set of keywords, we find a maximum Web query (containing the most keywords possible) that respects user-defined bounds on the number of returned hits. We assume a real-world setting where the user is not given direct access to a Web search engine's index, i.e., querying is possible only through an interface
We present an index structure to support the approximate keyword search in text databases. In an approximate keyword search query, the user presents a query word Q and a tolerance value k (kges0), and wishes to find all documents in the database that contain the query word Q or any other word in the vocabulary that
semantic net which can be applied to build personalized search engine and tested with single query keyword and multi ones by three different calculating policies. The test results show that it can affect the sort of pages. The personalized search based on vocabulary semantic net improves the quality of search results greatly.
search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted
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.