Methods and systems for determining a meaning of a document to match the document to content

• US 8,914,361 B2
• Filed: 10/21/2003
• Issued: 12/16/2014
• Est. Priority Date: 09/22/1999
• Status: Expired due to Term

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Weissman, Adam J., Elbaz, Gilad Israel
• Primary Examiner(s): Bhatia, Ajay
• Assistant Examiner(s): SHECHTMAN, CHERYL MARIA
• Application Number:
  US10/689,903
• Publication Number:
  US 20040243581A1
• Time in Patent Office: 4,074 Days
• Field of Search: 707/10, 707/3
• US Class Current: 707/729
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Systems and methods for determining a meaning of a document to match the document to content are described. In one aspect, a source article is accessed, a plurality of regions in the source article are identified, at least one local concept associated with each region is determined, the local concepts of each region are analyzed to identify any unrelated regions, the local concepts associated with any unrelated regions are eliminated to determine relevant concepts, the relevant concepts are analyzed to determine a source meaning for the source article, and the source meaning is matched with an item meaning associated with an item from a set of items.

Claims

1. A method for determining a source meaning for a web page document, the method performed by a document server implemented as a network of computer processors or as a single computer system, the document server executing a document engine, the method comprising:

receiving a web page document;

identifying, based on formatting information of the web page document, a collection of different regions contained within the webpage document, that would be displayed to a user visiting said web page document, wherein the regions contained within the webpage document contain content between opening and closing HTML or XML tags;

determining concepts expressed in each of the previously identified different regions in the collection,

wherein determining the concepts expressed in each of the different regions comprises

identifying words contained within each of the different regions and aligning the words with concepts;

determining scores for the concepts expressed in each of the different regions, wherein the score for a concept expressed in at least one of the different regions is based on a size of the at least one of the different regions;

creating a ranked global list of concepts based at least in part on said scores for said concepts expressed in each of said different regions;

removing unrelated concepts from said global list of concepts;

determining the source meaning for the web page document,

wherein determining the source meaning includes excluding the unrelated concepts from the determination of the source meaning and

wherein the source meaning is a vector of said determined concepts expressed in the web page document; and

making the previously determined source meaning available.

2. The method of claim 1, wherein identifying the collection of different regions in the document comprises identifying frames in the web page document.

3. The method of claim 1, wherein identifying the collection of different regions in the web page document comprises identifying the different regions based on markup in the web page document.

4. The method of claim 1, wherein determining concepts comprises identifying collections of related words in each of the different regions in the collection.

5. The method of claim 1, further comprising:

based on the concepts expressed in each of the different regions in the collection, selecting one or more advertisements; and

making the web page document and the selected advertisements available for output.

6. A computer-implemented method comprising:

receiving, by a computing system, a source web page document;

identifying, by the computing system based on formatting information of the source web page document, a plurality of regions contained within the source webpage document, that would be displayed to a user visiting said source web page document, wherein the regions contained within the source webpage document contain content between opening and closing HTML or XML tags;

determining, by the computing system, at least one local concept expressed within each previously identified region, wherein determining the at least one local concept comprises identifying words in the document and aligning the words with concepts, wherein said at least one local concept expressed within said each previously identified region is a concept expressed by two or more words contained within the region;

determining a score for a local concept expressed in each previously-identified region, wherein the score is based on an importance associated with the previously-identified region;

analyzing, by the computing system, the previously determined at least one local concept of each region to identify and eliminate from consideration one or more local concepts that are unrelated to local concepts of other of said previously identified regions by creating a ranked global list of all of said local concepts;

analyzing, by the computing system, the previously identified regions to identify and eliminate from consideration one or more regions that are unrelated to other previously identified regions by comparing a ranked list of local concepts for each of said previously identified regions to said global list;

determining, by the computing system, a source meaning for the source web page document, wherein the source meaning for the source web page document is a weighted vector of said previously determined local concepts expressed in the source web page document that remain after the eliminations; and

matching, by the computing system, the source web page document with an item selected from a set of items by comparing the previously determined source meaning and a meaning of the item.

7. The computer-implemented method of claim 6, further comprising causing the matched item to be displayed on the source web page document at a display device.

8. The computer-implemented method of claim 7, wherein the matched item comprises a keyword.

9. The computer-implemented method of claim 7, wherein the matched item comprises an advertisement.

10. The computer-implemented method of claim 6, further comprising displaying content associated with the matched item on the source web page document.

11. The computer-implemented method of claim 10, wherein:

the matched item comprises a keyword; and

the associated content comprises an advertisement.

12. The computer-implemented method of claim 10, wherein:

the matched item comprises a second web page; and

the associated content comprises an advertisement.

13. The computer-implemented method of claim 10, wherein:

the matched item comprises a second web page; and

the associated content comprises a link to the second web page.

14. The computer-implemented method of claim 6, wherein matching the source web page document with the item comprises matching the source web page document with the item using a biasing factor.

15. The computer-implemented method of claim 6, wherein:

determining at least one local concept comprises determining a score for each local concept; and

the local concept with the highest score in each region comprises a most relevant local concept for that region.

16. The computer-implemented method of claim 15, wherein identifying unrelated concepts comprise:

determining a revised score for each local concept;

determining said ranked global list of all local concepts based on the revised scores;

removing local concepts whose combined revised score contributes less than a predetermined amount of a total score of the global list to produce a resulting list;

identifying regions without a most relevant local concept on the resulting list; and

removing local concepts expressed in the identified regions without a most relevant local concept from the resulting list to produce a list of relevant concepts.

17. The computer-implemented method of claim 16, wherein determining the source meaning comprises normalizing the revised scores for the relevant concepts.

18. The computer-implemented method of claim 6, wherein identifying the plurality of regions comprises identifying content between said opening and closing HTML or XML tags as a region.

19. The computer-implemented method of claim 6, wherein determining at least one local concept comprises determining a score for at least one local concept based at least in part on a size of a region of the source web page document in which the at least one local concept is found.

20. The computer-implemented method of claim 6, wherein identifying the plurality of regions comprises dividing content that constitutes more than a predetermined percentage of the document, and that would otherwise be identifiable as a region, into multiple regions.

21. A non-transitory computer-readable medium storing program code operable to cause one or more computers to perform operations comprising:

receiving a source web page document;

identifying, by a preprocessor based on formatting information of the source web page document, a plurality of regions contained within the source webpage document, that would be displayed to a user visiting said source web page document, wherein the regions contained within the source webpage document contain content between opening and closing HTML or XML tags;

determining at least one local concept expressed in each previously identified region, wherein determining the at least one local concept comprises identifying words in the document and aligning the words with concepts, wherein said at least one local concept expressed in the previously identified region is expressed by two or more words in the region;

determining a score for a local concept expressed in each previously-identified region, wherein the score is based on a size of, or an importance associated with, the previously-identified region;

analyzing the previously determined at least one local concept of each region to identify and eliminate from consideration one or more local concepts that are unrelated to local concepts of other of said previously identified regions by creating a ranked global list of all of said local concepts;

analyzing the previously identified regions to identify and eliminate from consideration one or more regions that are unrelated to regions by comparing a ranked list of local concepts for each of said previously identified regions to said global list;

determining a source meaning for the source web page document, wherein the source meaning for the source web page document is a weighted vector of said previously determined local concepts expressed in the source web page document that remain after the eliminations; and

matching the source web page document with an item selected from a set of items by comparing the previously determined source meaning and a meaning of the item.

22. The non-transitory computer-readable medium of claim 21, wherein:

the matched item comprises a keyword; and

the associated content comprises an advertisement.

23. The non-transitory computer-readable medium of claim 21, wherein:

the matched item comprises a second web page; and

the associated content comprises an advertisement.

24. The non-transitory computer-readable medium of claim 21, wherein:

the matched item comprises a second web page; and

the associated content comprises a link to the second web page.

25. The non-transitory computer-readable medium of claim 21, wherein matching the source web page document with the item comprises matching the source web page document with the item using a biasing factor.

26. The non-transitory computer-readable medium of claim 21, wherein eliminating said unrelated concepts comprises ranking said previously determined local concepts in said global list using biasing factors.

27. The non-transitory computer-readable medium of claim 21, wherein:

determining at least one local concept comprises determining a score for each local concept; and

the local concept with the highest score in each region comprises a most relevant local concept for that region.

28. The non-transitory computer-readable medium of claim 27, wherein identifying unrelated concepts comprises:

determining a revised score for each local concept;

determining said ranked global list of all local concepts based on the revised scores;

removing local concepts whose combined revised score contributes less than a predetermined amount of a total score of the global list to produce a resulting list;

identifying unrelated regions without a most relevant local concept on the resulting list; and

removing local concepts expressed in the unrelated regions from the resulting list to produce a list of relevant concepts.

29. The non-transitory computer-readable medium of claim 28, wherein determining the source meaning comprises normalizing the revised scores for the relevant concepts.