Inference indexing

• US 8,977,625 B2
• Filed: 12/15/2010
• Issued: 03/10/2015
• Est. Priority Date: 12/15/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Buehrer, Gregory T., Jiang, Li, Viola, Paul Alfred, McGovern, Andrew Paul, Szymanski, Jakub Jan, Ahari, Sanaz
• Primary Examiner(s): LIAO, JASON G
• Application Number:
  US12/968,481
• Publication Number:
  US 20120158738A1
• Time in Patent Office: 1,546 Days
• Field of Search: None
• US Class Current: 707/741
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Methods, systems, and media are provided for facilitating generation of an inference index. In embodiments, a canonical entity is referenced. The canonical entity is associated with web documents. One or more queries that, when input, result in a selection of at least one of the web documents are identified. An entity document is generated for the canonical entity. The entity document includes the identified queries and/or associated text from the content of a document or from an entity title that result in the selection of the at least one of the web documents. The entity document and corresponding canonical entity can be combined with additional related entity documents and canonical entities to generate an inference index.

Claims

1. A computer-implemented method of facilitating generation of an inference index using a computing system having processor, memory, and data storage subsystems, the computer-implemented method comprising:

referencing a canonical entity that is associated with one or more web documents;

identifying a plurality of queries that, when input, result in a selection of at least one web document of the one or more web documents associated with the canonical entity;

generating, via the processor, an entity document for the canonical entity, the entity document including the plurality of identified queries and a representation of the at least one web document, wherein the plurality of identified queries resulted in the selection of the at least one web document corresponding with the canonical entity comprising a unique representation of an entity;

generating an inference index using the canonical entity and the entity document along with other related canonical entities and corresponding entity documents, the inference index corresponding with a knowledge domain of related canonical entities; and

utilizing the inference index in response to a real-time user query provided by a user after generation of the inference index to select a particular canonical entity that is most related to the real-time user query, the particular canonical entity comprising a unique representation of an entity that indicates a person or place and that is selected based on a cumulative score associated with the selected canonical entity being greater than cumulative scores associated with one or more other canonical entities within the inference index, wherein each of the cumulative scores for the canonical entities comprises an aggregate of entity document scores within the corresponding canonical entity, each entity document score calculated based on a frequency of at least a portion of the real-time user query occurring within queries of the corresponding entity document.

2. The computer-implemented method of claim 1, wherein the entity document includes the one or more web documents.

3. The computer-implemented method of claim 1, further comprising:

composing one or more queries which are mapped to the canonical entity;

searching the composed one or more queries to obtain one or more resultant web documents mapped to the canonical entity;

collecting stored queries linked to the one or more resultant web documents; and

combining the collected stored queries to the entity document for the canonical entity.

4. The computer-implemented method of claim 1, wherein the association of the one or more web documents with the canonical entity is based on a user query or a composed query.

5. The computer-implemented method of claim 1, wherein one or more uniform resource locators are associated with the canonical entity corresponding with the entity document.

6. A computer-readable media, wherein the computer-readable media is one or more computer storage devices having computer-executable instructions embodied thereon that when executed by a computing device, perform a method, the method comprising:

referencing a canonical entity that is associated with one or more web documents;

identifying a plurality of queries that, when input, result in a selection of at least one web document of the one or more web documents associated with the canonical entity;

generating, via the processor, an entity document for the canonical entity, the entity document including the plurality of identified queries and a representation of the at least one web document, wherein the plurality of identified queries resulted in the selection of the at least one web document corresponding with the canonical entity comprising a unique representation of an entity;

generating an inference index using the canonical entity and the entity document along with other related canonical entities and corresponding entity documents, the inference index corresponding with a knowledge domain of related canonical entities; and

utilizing the inference index in response to a real-time user query provided by a user after generation of the inference index to select a particular canonical entity that is most related to the real-time user query, the particular canonical entity comprising a unique representation of an entity that indicates a person or place and that is selected based on a cumulative score associated with the selected canonical entity being greater than cumulative scores associated with one or more other canonical entities within the inference index, wherein each of the cumulative scores for the canonical entities comprises an aggregate of entity document scores within the corresponding canonical entity, each entity document score calculated based on a frequency of at least a portion of the real-time user query occurring within queries of the corresponding entity document.

7. The media of claim 6, wherein the entity document includes the one or more web documents.

8. The media of claim 6, wherein the method further comprises:

composing one or more queries which are mapped to the canonical entity;

searching the composed one or more queries to obtain one or more resultant web documents mapped to the canonical entity;

collecting stored queries linked to the one or more resultant web documents; and

combining the collected stored queries to the entity document for the canonical entity.

9. The media of claim 6, wherein the association of the one or more web documents with the canonical entity is based on a user query or a composed query.

10. The media of claim 6, wherein one or more uniform resource locators are associated with the canonical entity corresponding with the entity document.