You can try the demo now, try out some sample queries , or read ahead for more detailed instructions. Select one of several small local database tables in the Rank field. Suppose you select the ACM SIGs as the local database. If you supply Knuth in the Near field, then you're creating a query to rank the ACM SIGs by how often each SIG appears on the Web near Knuth . If you leave the Near field empty, then you're creating a query to measure the pure popularity of each SIG on the Web, independent of context.
Rankings can be absolute or normalized .
With absolute rankings, tuples will be ranked simply by the number of times they appear on the Web together with the Near expression. With normalized rankings, the number of Web hits for the expression is normalized by the number of times the tuple appears on the Web without the Near expression. The motivation for this approach is best understood by considering the U.S. States. Now suppose we want to rank each state by how often it appears on the Web near the keyword crime . With absolute rankings, the most popular states will rank highly again since there are just so many more Web pages for those states.