Method for ranking and sorting electronic documents in a search result list based on relevance

• US 7,814,099 B2
• Filed: 01/31/2006
• Issued: 10/12/2010
• Est. Priority Date: 01/31/2006
• Status: Expired due to Fees

• Current Assignee: Daniel X. Wang, Louis S. Wang
• Original Assignee: Daniel X. Wang, Louis S. Wang
• Inventors: Wang, Louis S.
• Primary Examiner(s): Stace; Brent
• Application Number:
  US11/343,324
• Publication Number:
  US 20070179930A1
• Time in Patent Office: 1,715 Days
• Field of Search: 707 1- 4, 707/7, 707/705, 707/722, 707/723, 707/724, 707/727, 707/730
• US Class Current: 707/730
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A method of evaluating the relevance rank of a target document in a search result list, includes the steps of assigning a basic credit to all keywords that are found in the target document; grouping keywords that are found in the target document; assigning extra value of credit to each keyword group; calculating the total value of credit of the whole target document; calculating the maximum value of credit of the whole target document, and calculating the relevance rank according to a comparison function of the total value of credit and the maximum value of credit for the target document.

Claims

1. A method comprising:

running a query comprising at least two keywords in a documents database,

producing a search result list of target documents satisfying the query, and

providing said search result list to a computer program running in a computer,

wherein as to each target document in the search results list said computer program performs the steps of;

assigning a basic value of credit for each occurrence of a keyword that is found in the target document;

identifying keyword groups in the target document by grouping keywords that are found in the target document without regard to keyword order according to

a proximity relationship that groups each occurrence of a keyword with the next sequential occurrence of a keyword in the target document,

an adjacency relationship in the target document applied to all keywords in the query, or

both a said proximity relationship and a said adjacency relationship;

assigning an extra value of credit to each occurrence of a keyword group identified in the target document;

calculating a total value of credit of the target document, which total value of credit comprises summing each said basic value of credit assigned for an occurrence of a keyword in the target document and each said extra value of credit assigned for an occurrence of a keyword group in the target document;

determining a maximum value of credit of the target document; and

calculating a relevance rank according to a comparison function of the total value of credit and the maximum value of credit for the target document,

and wherein the target document has a total number of words, said search query has a number of keywords,

the step of assigning an extra value of credit to each occurrence of a keyword group comprises assigning an extra keyword value of credit for each keyword in each keyword group, and

the step of determining a maximum value of credit of the target document comprises multiplying

the total number of words in the target document by

the sum of the basic value of credit of a keyword and the extra keyword value of credit assigned to a keyword in a keyword group with the same number of keywords as the search query.

2. The method of claim 1, wherein calculating the relevance rank for the target document comprises dividing the total value of credit of the target document by said maximum value of credit.

3. The method of claim 1 wherein said assigning an extra value of credit to each keyword in said keyword group uses a reciprocal, linear, polynomial, rational, exponential or logarithmic mathematical function.

4. The method of claim 3 wherein said mathematical function, for a group length of a value n and a number of keywords of a value N the query, assigns extra value of credit for each word within the group as equal to

5. The method of claim 1 wherein the keywords are grouped only according to a said adjacency relationship.

6. The method of claim 1 wherein the keywords are grouped only according to a said proximity relationship.

7. The method of claim 1 wherein the keyword groups are assigned extra values of credit only according to both a said proximity relationship and a said adjacency relationship.

8. The method of claim 1 wherein the target document has a number of searchable words and the maximum value of credit of the target document is a function of the number of searchable words in the target document and of the number of keywords in the query.

9. A method comprising:

running a query comprising at least two keywords in a documents database,

producing a search result list of target documents satisfying the query, and

providing said search result list to a computer program running in a computer,

wherein as to each target document in the search results list said computer program performs the steps of;

assigning a basic value of credit for each occurrence of a keyword that is found in the target document;

identifying keyword groups in the target document by grouping keywords that are found in the target document without regard to keyword order according to

a proximity relationship that groups each occurrence of a keyword with the next sequential occurrence of a keyword in the target document,

an adjacency relationship in the target document applied to all keywords in the query, or

both a said proximity relationship and a said adjacency relationship;

assigning an extra value of credit to each occurrence of a keyword group identified in the target document;

calculating a total value of credit of the target document which total value of credit comprises summing each said basic value of credit assigned for an occurrence of a keyword in the target document and each said extra value of credit assigned for an occurrence of a keyword group in the target document;

determining a maximum value of credit of the target document; and

calculating a relevance rank according to a comparison function of the total value of credit and the maximum value of credit for the target document,

and wherein for a keyword group having a group length of n and a number of keywords N in the query, the total extra value of credit provided by the keyword group is

10. The method of claim 9 wherein the target document has a number of words of a value M and the maximum value of credit of the target document is

11. A method comprising:

running a query comprising at least two keywords in a documents database,

producing a search result list of target documents satisfying the query, and

providing said search result list to a computer program running in a computer,

wherein as to each target document in the search results list said computer program performs the steps of;

assigning a basic value of credit for each occurrence of a keyword that is found in the target document;

identifying keyword groups in the target document by grouping keywords that are found in the target document without regard to keyword order according to

a proximity relationship that groups each occurrence of a keyword with the next sequential occurrence of a keyword in the target document,

an adjacency relationship in the target document applied to all keywords in the query, or

both a said proximity relationship and a said adjacency relationship;

assigning an extra value of credit to each occurrence of a keyword group identified in the target document;

calculating a total value of credit of the target document, which total value of credit comprises summing each said basic value of credit assigned for an occurrence of a keyword in the target document and each said extra value of credit assigned for an occurrence of a keyword group in the target document;

determining a maximum value of credit of the target document; and

calculating a relevance rank according to a comparison function of the total value of credit and the maximum value of credit for the target document,

and wherein the target document has a total number of words of a value M, the search query has a number of words of a value N, each keyword group has a group length of a value n, each keyword group is taken as a Type n Group wherein Type n is a value equal to n when n≦

N and Type n is equal to N when n>

N, for each Type n, the target document has a total number of Type n Group keywords of a value M_n and the relevance rank is a value Rel calculated according to the formula;

12. A computer programmed with a database search engine that performs the following steps in response to a search query from a user:

the computer providing a search result list of target documents conforming to a search query, and,

as to each target document in the search result list, the computer;

assigning a basic value of credit for each occurrence of a keyword that is found in the target document;

identifying keyword groups in the target document by grouping keywords that are found in the target document without regard to keyword order according to

a proximity relationship that groups each occurrence of a keyword with the next sequential occurrence of a keyword in the target document,

an adjacency relationship in the target document applied to all keywords in the query, or

both a said proximity relationship and a said adjacency relationship;

assigning an extra value of credit to each occurrence of a keyword group identified in the target document;

calculating a total value of credit of the target document, which total value of credit comprises summing each said basic value of credit assigned for an occurrence of a keyword in the target document and each said extra value of credit assigned for an occurrence of a keyword group in the target document;

determining a maximum value of credit of the target document; and

calculating a relevance rank according to a comparison function of the total value of credit and the maximum value of credit for the target document,

and wherein the target document has a total number of words, said search query has a number of keywords,

the step of assigning an extra value of credit to each occurrence of a keyword group comprises assigning an extra keyword value of credit for each keyword in each keyword group, and

the step of determining a maximum value of credit of the target document comprises multiplying

the total number of words in the target document by

the sum of the basic value of credit of a keyword and the extra keyword value of credit assigned to a keyword in a keyword group with the same number of keywords as the search query.

13. A programmed computer as in claim 12 wherein the search result list is provided to a display in order of said relevance rank.