SEARCH RESULTS BY MAPPING ASSOCIATED WITH DISPARATE TAXONOMIES

• US 20130238608A1
• Filed: 03/07/2012
• Published: 09/12/2013
• Est. Priority Date: 03/07/2012
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Bennett, Paul Nathan, Svore, Krysta M., Sia, Ka Cheung, Tropin, Kyrylo, Middha, Bhuvan
• Application Number:
  US13/413,651
• Publication Number:
  US 20130238608A1
• Time in Patent Office: Days
• US Class Current: 707/723
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence

Abstract

Architecture that generates signals/features that capture the match between intent of a query and category of documents. For example, for a query intent related to “autos”, documents that belong to categories related to “Autos” receive a higher score than documents of a “computers” category. The architecture can be applied to a search ecosystem where query intent classification and document category classifier are available, learns the mapping between query intent and document category, and introduces category-match features to a ranking algorithm, thereby improving search result relevance. The architecture learns the mapping between two existing and different taxonomies to create a category match signal from which the ranking algorithm can learn. Moreover, architecture adapts to a complex ecosystem where different taxonomies on the query side and document side exist through learning a mapping score between at least two taxonomies.

Claims

1. A system, comprising:

a mapping component

that generates mappings between items of different taxonomies or mappings between items of a single taxonomy, and

that computes the mappings as a probability that the items are related, the probability computed by dividing a number of relevant documents by a number of all documents and document categories of all of the documents are the same as a class of the query intent;

a learning component that learns the mappings and outputs feature values for use by a ranking algorithm; and

a processor that executes computer-executable instructions associated with at least one of the mapping component or the learning component.

2. The system of claim 1, wherein the items of the different taxonomies are query intent of a first taxonomy and categories of results of a second taxonomy.

3. The system of claim 2, wherein the mapping component computes query intent entropy over all items of query intent.

4. The system of claim 1, wherein the items of the single taxonomy are categories of results of query intent of a query.

5. The system of claim 1, wherein the mappings are characterized as scores that are ranked to select an optimum mapping.

7. The system of claim 1, wherein the mapping component translates classes derived by a classifier between items of the taxonomies or between categories of the single taxonomy.

8. The system of claim 7, wherein the mapping component applies a threshold to limit membership of query intent and the classes.

6. (canceled)

9. A method, comprising acts of:

receiving a taxonomy of items related to a query and a different taxonomy of items related to search results;

creating mappings between items of different taxonomies or mappings between items of a single taxonomy by computing the mappings as a probability that the items are related, the probability computed by dividing a number of relevant documents by a number of all documents, and document categories of all of the documents are the same as the class of a query intent;

learning the mappings;

generating a match signal from the mappings for use in a ranking algorithm; and

utilizing a processor that executes instructions stored in memory to perform at least one of the acts of receiving, creating, learning, or generating.

10. The method of claim 9, further comprising creating the mappings between items of the taxonomy.

11. The method of claim 9, further comprising creating the mappings between the items of the taxonomy and the items of the different taxonomy.

12. The method of claim 9, further comprising creating the mappings between items of the taxonomy and, creating mappings between the items of the taxonomy and the items of the different taxonomy.

13. The method of claim 9, further comprising computing query intent entropy over all items of query intent related to the query.

14. The method of claim 9, further comprising applying a threshold to limit membership of query intent and categories of the search results.

15. The method of claim 9, further comprising training the ranking algorithm using the match signal.

16. A method, comprising acts of:

receiving query intent of a query of a first taxonomy and documents of a different taxonomy, the documents returned in association with processing of the query;

classifying the documents into document categories;

creating a mapping between the document categories and the query intent based on mapping data, the mapping data being a translation model that estimates predictions between document categories and the query intent;

generating feature signals from the mapping data for use in a ranker algorithm; and

utilizing a processor that executes instructions stored in memory to perform at least one of the acts of receiving, classifying, creating, or generating.

17. The method of claim 16, further comprising employing classifier algorithms to derive the query intent and classify the document categories.

18. The method of claim 16, further comprising computing the mapping data as a probability that the documents are related to the query intent.

19. The method of claim 16, further comprising training the ranking algorithm using the feature signals and other training data.

20. The method of claim 16, further comprising computing a feature signal per each item of query intent.