Document ranking with sub-query series

• US 7,849,077 B2
• Filed: 07/06/2006
• Issued: 12/07/2010
• Est. Priority Date: 07/06/2006
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Liao, Ciya, Alpha, Shamim A.
• Primary Examiner(s): Ali; Mohammad
• Assistant Examiner(s): Bui; Thuy (Tiffany)
• Application Number:
  US11/481,686
• Publication Number:
  US 20080010268A1
• Time in Patent Office: 1,615 Days
• Field of Search: 707 1- 3, 707/4, 707/5, 707/7, 707/10, 707/705, 707/706, 707/707, 707/765, 707/766, 707/767, 707/768
• US Class Current: 707/718
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Systems, methods, media, and other embodiments associated with ranking documents by providing a search engine with a series of sub-queries generated from an original query are described. One example system includes input logic for receiving a query. The example system may include a relaxation logic configured to produce sub-queries from the query. The sub-queries may describe metadata string matching, content string matching, and/or metadata numerical attribute analysis. The sub-queries may be provided by an output logic to a search engine in an order that facilitates defining document relevance without requiring post-retrieval relevance ranking.

Claims

1. A computer implemented method, comprising:

receiving a query, the query being configured to cause a search logic to retrieve one or more documents, the documents comprising text elements and metadata elements;

creating from the query a series of sub-queries {SQ₁, . . . SQ_N}, N being an integer greater than 1, the series of sub-queries including a sub-query based on one or more of a numeric metadata link score and a numeric metadata URL depth;

the series of sub-queries being configured so that a sub-query SQ_X will cause the search logic to retrieve fewer documents than a sub-query SQ_Y when X<

Y;

the series of sub-queries being configured so that a sub-query SQ_A will cause the search logic to retrieve documents having a higher relevancy than a sub-query SQ_B when A<

B; and

providing, in order according to the configuration of the series of sub-queries, one or more sub-queries from the series of sub-queries to the search logic until a pre-determined number of documents are retrieved by the search logic.

2. The method of claim 1, where the series of sub-queries includes a metadata-centric sub-query and further where the metadata-centric sub-query comprises one or more of, a sub-query that matches terms in the query to one or more metadata elements on a phrase level, a sub-query that matches terms in the query to one or more metadata elements on a proximity level, and a sub-query that matches terms in the query to one or more metadata elements on a Boolean AND level.

3. The method of claim 2, where the metadata elements include one or more of, a title, a description, a keyword, a subject, an author, a uniform resource locator (URL), an H1 headline, an H2 headline, and an anchor text.

4. The method of claim 2, where the metadata-centric sub-query includes at least one metadata element associated with a metadata element weight, and where the document relevance is a function of:

a type of match between terms in the query and the metadata element.

5. The method of claim 1, where the series of sub-queries includes a linguistic-centric sub-query and further where the linguistic-centric sub-query includes one or more of, a sub-query that matches terms in the query to one or more text elements on a phrase level, a sub-query that matches terms in the query to one or more text elements on a proximity level, and a sub-query that matches terms in the query to one or more text elements on a Boolean AND level.

6. The method of claim 1, including creating a sub-query by stemming terms in the query.

7. The method of claim 1, where the series of sub-queries includes a metadata-centric sub-query and a linguistic-centric sub-query, ordered:

to make matches between terms in the query and metadata elements more determinative of document relevancy than matches between terms in the query and text elements;

to make a phrase match more determinative of document relevancy than a proximity match; and

to make a proximity match more determinative of document relevancy than a Boolean AND match.

8. The method of claim 1, where document relevance is a function of a metadata element weight and further including establishing the following metadata element to metadata element weight pairs:

(title, w₁), (description, w₂), (keyword, w₃), (subject, w₄), (author, w₅), (url, w₆), (H1 headline, w₇), (H2 headline, w₈), and (anchortext, w₉), where w₁ through w₉ are configurable to facilitate determining the document relevance before document retrieval.

9. The method of claim 8, where w₁>

w₂>

w₃>

w₄>

w₅>

w₆>

w₇>

w₈>

w₉.

10. The method of claim 1, where the numeric metadata link score is directly proportional to document relevance and where the numeric metadata URL depth is inversely proportional to document relevance.

11. The method of claim 1, further comprising

providing retrieved documents to a user, the documents being ordered based on relevance without performing post-retrieval relevance ranking and where duplicate documents are removed.

12. A computer implemented method, comprising:

receiving a query, the query being configured to cause a search logic to retrieve one or more documents, the documents comprising text elements and metadata elements;

creating from the query a series of sub-queries {SQ₁, . . . SQ_N}, N being an integer greater than 1, the series of sub-queries including a sub-query based on one or more of, a numeric metadata link score, and a numeric metadata URL depth, where the numeric metadata link score is directly proportional to document relevance and where the numeric metadata URL depth is inversely proportional to document relevance;

the series of sub-queries being configured so that a sub-query SQ_X will cause the search logic to retrieve fewer documents than a sub-query SQ_Y when X<

Y;

the series of sub-queries being configured so that a sub-query SQ_A will cause the search logic to retrieve documents having a higher relevancy than a sub-query SQ_B when A<

B; and

providing, in order according to the configuration of the series of sub-queries, one or more sub-queries from the series of sub-queries to the search logic until a pre-determined number of documents are retrieved by the search logic.

13. A non-transitory computer-readable storage medium storing processor executable instructions operable to perform a method, the method comprising:

receiving a query, the query being configured to cause a search logic to retrieve one or more documents, the documents comprising text elements and metadata elements;

creating from the query a series of sub-queries {SQ₁, . . . SQ_N}, N being an integer greater than 1, the series of sub-queries including one or more of, a sub-query based on one or more of, a numeric metadata link score, and a numeric metadata URL depth, where the numeric metadata link score is directly proportional to document relevance and where the numeric metadata URL depth is inversely proportional to document relevance;

the series of sub-queries being configured so that a sub-query SQ_X will cause the search logic to retrieve fewer documents than a sub-query SQ_Y when X<

Y;

the series of sub-queries being configured so that a sub-query SQA will cause the search logic to retrieve documents having a higher relevancy than a sub-query SQB when A<

B; and

providing, in order providing, in order one or more sub-queries from the series of sub-queries, one or more sub-queries from the series of sub-queries to the search logic until a pre-determined number of documents are retrieved by the search logic.

14. The non-transitory computer-readable medium of claim 13, where the series of sub-queries includes a metadata-centric sub-query and further where the metadata-centric sub-query comprises one or more of, a sub-query that matches terms in the query to one or more metadata elements on a phrase level, a sub-query that matches terms in the query to one or more metadata elements on a proximity level, and a sub-query that matches terms in the query to one or more metadata elements on a Boolean AND level.

15. The non-transitory computer-readable medium of claim 14, where the metadata elements include one or more of, a title, a description, a keyword, a subject, an author, a uniform resource locator (URL), an H1 headline, an H2 headline, and an anchor text.

16. The non-transitory computer-readable medium of claim 14, where the metadata-centric sub-query includes at least one metadata element associated with a metadata element weight, and where the document relevance is a function of a type of match between terms in the query and the metadata element.

17. The non-transitory computer-readable medium of claim 13, where the series of sub-queries includes a linguistic-centric sub-query and further where the linguistic-centric sub-query includes one or more of, a sub-query that matches terms in the query to one or more text elements on a phrase level, a sub-query that matches terms in the query to one or more text elements on a proximity level, and a sub-query that matches terms in the query to one or more text elements on a Boolean AND level.

18. The non-transitory computer-readable medium of claim 13, where the instructions include creating a sub-query by stemming terms in the query.

19. The non-transitory computer-readable medium of claim 13, where the series of sub-queries includes a metadata-centric sub-query and a linguistic-centric sub-query, ordered:

to make matches between terms in the query and metadata elements more determinative of document relevancy than matches between terms in the query and text elements;

to make a phrase match more determinative of document relevancy than a proximity match; and

to make a proximity match more determinative of document relevancy than a Boolean AND match.

20. The non-transitory computer-readable medium of claim 13, where document relevance is a function of a metadata element weight and further including establishing the following metadata element to metadata element weight pairs:

(title, w₁), (description, w₂), (keyword, w₃), (subject, w₄), (author, w₅), (url, w₆), (H1 headline, w₇), (H2 headline, w₈), and (anchortext, w₉), where w₁ through w₉ are configurable to facilitate determining the document relevance before document retrieval.

21. The non-transitory computer-readable medium of claim 20, where w₁>

w₂>

w₃>

w₄>

w₅>

w₆>

w₇>

w₈>

w₉.

22. The non-transitory computer-readable medium of claim 13, comprising providing retrieved documents to a user, the documents being ordered based on relevance without performing post-retrieval relevance ranking and where duplicate documents are removed.