Due to the developments in computer technology and its increase in popularity, large numbers of people have recently started to frequently search huge databases. For example, internet search engines are frequently used to search the entire world wide web. Currently, a popular search engine might execute over 30 million searches per day of the indexable part of the web, which has a size in excess of 500 Gigabytes. Information retrieval systems are traditionally judged by their precision and recall. What is often neglected, however, is the quality of the results produced by these search engines. Large databases of documents such as the web contain many low quality documents. As a result, searches typically return hundreds of irrelevant or unwanted documents which camouflage the few relevant ones. In order to improve the selectivity of the results, common techniques allow the user to constrain the scope of the search to a specified subset of the database, or to provide additional search terms. These techniques are most effective in cases where the database is homogeneous and already classified into subsets, or in cases where the user is searching for well known and specific information. In other cases, however, these techniques are often not effective because each constraint introduced by the user increases the chances that the desired information will be inadvertently eliminated from the search results.
Search engines presently use various techniques that attempt to present more relevant documents. Typically, documents are ranked according to variations of a standard vector space model. These variations could include (a) how recently the document was updated, and/or (b) how close the search terms are to the beginning of the document. Although this strategy provides search results that are better than with no ranking at all, the results still have relatively low quality. Moreover, when searching the highly competitive web, this measure of relevancy is vulnerable to “spamming” techniques that authors can use to artificially inflate their document's relevance in order to draw attention to it or its advertisements. For this reason search results often contain commercial appeals that should not be considered a match to the query. Although search engines are designed to avoid such ruses, poorly conceived mechanisms can result in disappointing failures to retrieve desired information.
Hyperlink Search Engine, developed by IDD Information Services, (http://rankdex.gari.com/) uses backlink information (i.e., information from pages that contain links to the current page) to assist in identifying relevant web documents. Rather than using the content of a document to determine relevance, the technique uses the anchor text of links to the document to characterize the relevance of a document. The idea of associating anchor text with the page the text points to was first implemented in the World Wide Web Worm (Oliver A. McBryan, GENVL and WWWW: Tools for Taming the Web, First International Conference on the World Wide Web, CERN, Geneva, May 25–27, 1994). The Hyperlink Search Engine has applied this idea to assist in determining document relevance in a search. In particular, search query terms are compared to a collection of anchor text descriptions that point to the page, rather than to a keyword index of the page content. A rank is then assigned to a document based on the degree to which the search terms match the anchor descriptions in its backlink documents.
The well known idea of citation counting is a simple method for determining the importance of a document by counting its number of citations, or backlinks. The citation rank r(A) of a document which has n backlink pages is simplyr(A)=n. 
In the case of databases whose content is of relatively uniform quality and importance it is valid to assume that a highly cited document should be of greater interest than a document with only one or two citations. Many databases, however, have extreme variations in the quality and importance of documents. In these cases, citation ranking is overly simplistic. For example, citation ranking will give the same rank to a document that is cited once on an obscure page as to a similar document that is cited once on a well-known and highly respected page.
USPTO Backgrounds
USPTO Backgrounds