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

• US 20070179930A1
• Filed: 01/31/2006
• Published: 08/02/2007
• Est. Priority Date: 01/31/2006
• Status: Active Grant

• Current Assignee: Daniel X. Wang, Louis S. Wang
• Original Assignee: Daniel X. Wang, Louis S. Wang
• Inventors: Wang, Daniel, Wang, Louis
• Granted Patent: US 7,814,099 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 of evaluating the relevance rank of a target document in a search result list, comprising:

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;

determining a 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.

2. The method of claim 1, wherein the step of calculating extra credit for each keyword group assigns said extra credit based on number of keywords within the group.

3. The method of claim 1, wherein the step of calculating the total value of the target document comprises summing credits, including basic and extra credits, of all keywords found in the target document.

4. The method of claim 1, wherein step of determining a maximum value of credit of the target document comprises multiplying the total number of the words in the target document and the value of credit of a keyword in the group with the same number of keywords in a search query.

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

6. The method of claim 1 wherein the number of search term keywords in a group with length n is a value n, and n is greater than 1.

7. The method of claim 6 wherein the step of assigning extra value of credit to each keyword in a group is assigned according to a reciprocal, linear, polynomial, rational, exponential or logarithmic mathematical function.

8. The method of claim 7 wherein said mathematical function for a group length of n and a number of keywords N in the query, assigns extra credit for each word within the group as equal to

$\sum _{}^{}$

i = 1 n - 1 ⁢

1 i ⁢

 

⁢

if ⁢

 

⁢

2 ≤

n ≤

N ⁢

 

⁢

or ⁢

 

⁢

∑

i = 1 N - 1 ⁢

1 i ⁢

 

⁢

if ⁢

 

⁢

n >

N .

9. The method of claim 1 wherein for a group length of n and a number of keywords N in the query, the total extra value of credit provided by the whole group is

$n$

∑

i = 1 n - 1 ⁢

1 i ⁢

 

⁢

if ⁢

 

⁢

2 ≤

n ≤

N ⁢

 

⁢

or ⁢

 

⁢

n ⁢

∑

i = 1 N - 1 ⁢

1 i ⁢

 

⁢

if ⁢

 

⁢

n >

N .

10. The method of claim 9 wherein the maximum value of credit of the whole target document is

$M$

( 1 + ∑

i = 1 N - 1 ⁢

1 i . )

where M is the number of words in the target document.

11. The method of claim 1 wherein the relevance rank is calculated according to the formula:

$\mathrm{Rel}=\frac{M_1+\underset{n=2}{\overset{N}{\sum <br><br>}}<br><br>\left[M_n<br><br>\left(1+\underset{i=1}{\overset{n-1}{\sum <br><br>}}<br><br>\frac{1}{i}\right)\right]}{M<br><br>\left(1+\underset{i=1}{\overset{N-1}{\sum <br><br>}}<br><br>\frac{1}{i}\right)}$

Rel is the relevance rank

M is the number of words in a target document,

M_n is the total number of Type n Group keywords in target document, and

n is the number of keywords in a Type n Group when n≦

N;

when n>

N, all Type n Groups are Type N Group

13. The method of claim 1 wherein the keywords are grouped on the basis of adjacency of words in the target document.

14. The method of claim 1 wherein the keywords are grouped on the basis of a preselected proximity factor.

15. The method of claim 1 wherein the keywords are assigned extra credits on the basis of a combination of adjacency and proximity factors.

16. The method of claim 1 wherein 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 search query.

12. A computer implemented method of evaluating the relevance rank of a target document, comprising the process illustrated in FIG. 1.

17. A computer software implementation method comprising the code blocks written in Microsoft C# Language illustrated in this document or a substantial translation thereof into another Language.

18. A method as in claim 17 wherein said another Language is a member of the group consisting of any computerized languages including but not limited to C⁺⁺, C⁺, C, VB.NET, VBScript, Visual Basic, FORTRAN and Assembler.

19. A database search engine performing the following steps in response to a search query from a user:

providing a search result list of target documents conforming to a search query, and, as to each target document in the search result list;

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;

determining a maximum value of credit of the 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.

20. A database search engine as in claim 19 wherein the search results list is provided to a display in order of said relevance rank.