Search query categorization for business listings search

• US 20040260677A1
• Filed: 06/17/2003
• Published: 12/23/2004
• Est. Priority Date: 06/17/2003
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Miittal, Vibhu, Malpani, Radhika
• Application Number:
  US10/462,818
• Publication Number:
  US 20040260677A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/353 into predefined classes

Abstract

A category classification component locates appropriate categories that apply to a user search query. The categories may be yellow page business listings. The category classification component may include a category model that is automatically trained on one or more of a number of possible training data sources. The training data sources may include directory listings, web documents, query traffic, and advertisement traffic.

Claims

1. A method for identifying categories relevant to a search query, the method comprising:

receiving the search query;

inputting the search query to a classification component that includes a category model trained with training data from one or more sources of information that relate terms to categories;

receiving one or more categories from the classification component in response to the search query; and

transmitting the one or more categories.

2. The method of claim 1, wherein the categories are business listing categories.

3. The method of claim 1, wherein the classification component uses Bayes-based classification techniques.

4. The method of claim 1, wherein the one or more sources of information include predefined yellow page directory listings.

5. The method of claim 4, further including:

extracting the training data from the predefined yellow page directory listings as business names corresponding to the terms and business categories associated with the business names.

6. The method of claim 1, wherein the one or more sources of information include pre-categorized business web sites.

7. The method of claim 6, wherein the business web sites are associated with at least one of the business categories based on information in a predefined yellow page directory listing.

8. The method of claim 6, wherein terms in the business web site are used to modify the category model.

9. The method of claim 8, wherein the terms in the business web site are used to modify the category model based on an inverse document frequency of the terms in the business web site.

10. The method of claim 1, wherein the one or more sources of information include at least one of consumer reports information and restaurant guides.

11. The method of claim 1, wherein the one or more sources of information include query traffic data.

12. The method of claim 11, wherein the classification component uses the query traffic data to infer likelihoods of ambiguous terms in the category model.

13. The method of claim 11, further comprising:

dynamically updating probabilities in the category model based on the query traffic data.

14. The method of claim 1, wherein the one or more sources of information include advertisement traffic data.

15. The method of claim 14, further comprising:

dynamically updating probabilities in the category model based on the advertisement traffic data.

16. A category classification device comprising:

a category classification component configured to implement a statistical model that associates search queries to business categories relevant to the search queries, the category classification component operating in a first mode in which the category classification component learns the associations between the search queries and the business categories based on training data and in a second mode in which the category classification component generates relevant business categories in response to input search queries; and

a category model configured to store the associations between the search queries and the business categories as a set of probabilities, the category model being constructed based on training data selected from at least one of predefined yellow page listings, categorized business web sites, consumer reports information, restaurant guides, query traffic data, and advertisement traffic data.

17. The category classification device of claim 16, wherein the category classification component implements a statistical classifier.

18. The category classification device of claim 16, wherein the category classification component dynamically updates the probabilities in the category model based on the query traffic data or the advertisement traffic data.

19. A computing device comprising:

a processor; and

a memory coupled to the processor, the memory including

a category classification component configured to implement a statistical model that associates search queries to business categories relevant to the search queries, the category classification component operating in a first mode in which the category classification component learns the associations between the search queries and the business categories based on training data and in a second mode in which the category classification component generates relevant business categories in response to input search queries, the training data being selected from at least one of pre-defined yellow page listings, categorized business web sites, consumer reports information, restaurant guides, query traffic data, and advertisement traffic data; and

a category model configured to store the associations between the search queries and the business categories as a set of probabilities, the category model being constructed based on the training data.

20. The computing device of claim 19, wherein the category classification component implements statistical classification.

21. The computing device of claim 19, wherein the category classification component dynamically updates the probabilities in the category model based on the query traffic data or the advertisement traffic data.

22. A method of training a model to associate categories with search queries, the method comprising:

receiving training data as a set of category entries each associated with a search query, each search query being represented by one or more search terms; and

automatically generating a statistical classification model based on the training data as a set of values that define probabilities of the search terms being associated with particular ones of the category entries.

23. The method of claim 22, wherein the training data includes predefined directory listings.

24. The method of claim 22, further comprising:

extracting the training data from the predefined directory listings as business names corresponding to the search terms and business categories corresponding to the category entries.

25. The method of claim 22, wherein the training data includes pre-categorized business web sites.

26. The method of claim 25, wherein terms in the business web sites are used to modify the category model based on an inverse document frequency of the terms in the business web site.

27. The method of claim 22, wherein the training data includes at least one of consumer reports information and restaurant guides.

28. The method of claim 22, wherein the training data includes query traffic data.

29. The method of claim 22, wherein the training data includes advertisement traffic data.

30. The method of claim 22, wherein the category entries are entries that define business categories.

31. A device comprising:

means for generating training data as a set of business category entries each associated with a search query, each search query being represented by one or more search terms; and

means for generating a category model based on the training data as a set of values that define probabilities of the search terms being associated with particular ones of the business category entries.

32. A computer-readable medium containing program instructions that when executed by a processor cause the processor to:

generate training data as a set of business category entries each associated with a search query, each search query being represented by one or more search terms; and

create a statistical classifier model based on the training data as a set of values that define probabilities of the search terms being associated with particular ones of the business category entries.