High scale adaptive search systems and methods

• US 20060287993A1
• Filed: 06/21/2005
• Published: 12/21/2006
• Est. Priority Date: 06/21/2005
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Liu, Jun, Katariya, Sanjeev, Yao, Qi
• Granted Patent: US 7,627,564 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

The subject invention relates to systems and methods that employ automated learning techniques to database and information retrieval systems in order to facilitate knowledge capabilities for users and systems. In one aspect, an adaptive information retrieval system is provided. The system includes a database component to store structured and unstructured data values. A search component queries the data values from the database, wherein a learning component associated with the search component or the database component is provided to facilitate retrieval of desired information.

Claims

1. An adaptive information retrieval system, comprising:

a database component to store structured and unstructured data values;

a search component to query the data values from the database; and

a learning component associated with the search component or the database component to facilitate retrieval of desired information

2. The system of claim 1, the learning component employs at least one Bayesian classifier.

3. The system of claim 1, the search component is associated with a natural language processor to facilitate information processing.

4. The system of claim 1, the data values are associated explicit or implicit feedback.

5. The system of claim 4, the implicit feedback is associated with user selections, user dwell times, file manipulation operations, computer system information or contextual data.

6. The system of claim 1, the database component is a relational database.

7. The system of claim 1, the learning component is employed to infer a user'"'"'s information goal.

8. The system of claim 7, the learning component is employed to execute an application function upon an inference of the user'"'"'s information goal.

9. The system of claim 1, further comprising a component to analyze an asset, an asset collection, an asset structure, and a feedback property.

10. The system of claim 1, further comprising a dynamic ranking component to determine a quality of match computation for a retrieved page and a query.

11. The system of claim 10, further comprising a component to determine an overall rank from the quality of match computation, a static rank, and a quality of page computation.

12. The system of claim 1, further comprising a component to initiate a dialog with a user to mitigate system uncertainty.

13. The system of claim 1, further comprising a semantic reasoning engine to analyze implicit or explicit feedback from a user.

14. The system of claim 1, further comprising a to determine structural probabilities to determine at least one of a top node, a coverage, a compactness, a depth, an inheritance, an attachment, and a dangling relationship.

15. The system of claim 1, further comprising at least one of a user interface and an application programming interface to interact with the learning component or the search component.

16. A computer readable medium having computer readable instructions stored thereon for implementing the components of claim 1.

17. An automated information retrieval method, comprising:

constructing at least one classifier from structured or unstructured database values;

applying the classifier to at least one search tool; and

automatically determining a subset of future search results in accordance with the classifier.

18. The method of claim 17, further comprising automatically analyzing explicit or implicit feedback to determine a quality factor for a search.

19. The method of claim 17, further comprising automatically determining an information goal for a user.

20. A system to facilitate computer information retrieval operations, comprising:

means for analyzing implicit or explicit feedback from user search activities;

means for learning from the feedback;

means for ranking future search results based upon the learning; and

means for automatically querying a database of structured or unstructured data values based on the learning and the feedback.