Ranking search results by reranking the results based on...

Electrical computers and digital processing systems: multicomput – Remote data accessing – Accessing a remote server

Reexamination Certificate

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Details

C707S793000, C707S793000

Reexamination Certificate

active

06725259

ABSTRACT:

BACKGROUND OF THE INVENTION
A. Field of the Invention
The present invention relates generally to the ranking of search results and, more particularly, to search engines that intelligently rank web pages based on a search query.
B. Description of Related Art
The World Wide Web (“web”) contains a vast amount of information. Locating a desired portion of the information, however, can be challenging. This problem is compounded because the amount of information on the web and the number of new users inexperienced at web searching are growing rapidly.
Search engines attempt to return hyperlinks to web pages in which a user is interested. Generally, search engines base their determination of the user's interest on search terms (called a search query) entered by the user. The goal of the search engine is to provide links to high quality, relevant results to the user based on the search query. Typically, the search engine accomplishes this by matching the terms in the search query to a corpus of pre-stored web pages. Web pages that contain the user's search terms are “hits” and are returned to the user.
In an attempt to increase the relevancy and quality of the web pages returned to the user, a search engine may attempt to sort the list of hits so that the most relevant and/or highest quality pages are at the top of the list of hits returned to the user. For example, the search engine may assign a rank or score to each hit, where the score is designed to correspond to the relevance or importance of the web page. Determining appropriate scores can be a difficult task. For one thing, the importance of a web page to the user is inherently subjective and depends on the user's interests, knowledge, and attitudes. There is, however, much that can be determined objectively about the relative importance of a web page. Conventional methods of determining relevance are based on the contents of the web page. More advanced techniques determine the importance of a web page based on more than the content of the web page. For example, one known method, described in the article entitled “The Anatomy of a Large-Scale Hypertextual Search Engine,” by Sergey Brin and Lawrence Page, assigns a degree of importance to a web page based on the link structure of the web page. In other words, the Brin and Page algorithm attempts to quantify the importance of a web page based on more than just the content of the web page.
The overriding goal of a search engine is to return the most desirable set of links for any particular search query. Thus, it is desirable to improve the ranking algorithm used by search engines and to therefore provide users with better search results.
SUMMARY OF THE INVENTION
Systems and methods consistent with the present invention address this and other needs by providing an improved search engine that refines a document's relevance score based on inter-connectivity of the document within a set of relevant documents.
In one aspect, the present invention is directed to a method of identifying documents relevant to a search query. The method includes generating an initial set of relevant documents from a corpus based on a matching of terms in a search query to the corpus. Further, the method ranks the generated set of documents to obtain a relevance score for each document and calculates a local score value for the documents in the generated set, the local score value quantifying an amount that the documents are referenced by other documents in the generated set of documents. Finally, the method refines the relevance scores for the documents in the generated set based on the local score values.


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patent: 5907840 (1999-05-01), Evans
patent: 5913208 (1999-06-01), Brown et al.
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patent: 6070158 (2000-05-01), Kirsch et al.
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“Automatic Resource Compilation by Analyzing Hyperlink Structure and Associated Text”; Soumen Chakrabarti et al.; 1998; pp. 1-14.
“The Anatomy of a large-Scale Hypertextual Web Search Engine”; Sergey Brin et al.; print date Aug. 7, 2000; pp. 1-20.
“Hilltop: A Search Engine Based on Expert Documents”; Krishna Bharat; Feb. 2000; pp. 1-12.
“Does “Authority” Mean Quality” Predicting Expert Quality Ratings of Web Documents; Brian Amento et al.; Jul. 2000; pp. 296-303.

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