Using a community generated web site for metadata

• US 20070271274A1
• Filed: 05/16/2006
• Published: 11/22/2007
• Est. Priority Date: 05/16/2006
• Status: Abandoned Application

• Current Assignee: Sony Corporation (Sony Group Corp.), Sony Electronics Inc. (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.), Sony Electronics Inc. (Sony Group Corp.)
• Inventors: Plutowski, Mark, Purang, Khemdut
• Application Number:
  US11/436,011
• Publication Number:
  US 20070271274A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Abstract

A category dataset includes names of categories and relation data, where the relation data defines a relationship between the categories and content. The categories for the content are generated by retrieving a web page from a an online community generated web site, such as the, WIKIPEDIA web site, associated with a particular piece of content and analyzing the web page for content metadata. The category data for that piece of content is extracted from the content metadata. In addition, the terms in category dataset are reduced based on the categories and the relation data.

Claims

1. A computerized method comprising:

receiving a web page from a community-generated web site, the web page associated with a particular piece of content;

extracting a plurality of terms from the web page;

adding the plurality of terms to content metadata associated with the piece of content;

extracting specific category data from the content metadata;

loading the specific category data into a category dataset; and

reducing a dimensionality of the category dataset based on the category dataset and relation data, wherein the relation data defines a relationship between the category dataset and the content associated with the category dataset.

2. The computerized method of claim 1, wherein extracting the plurality of terms further comprises at least one of stemming the terms in the web page, removing the stop terms from the web page, and extracting a limited number of terms from the web page.

3. The computerized method of claim 1, wherein extracting the plurality of terms further comprises defining parser actions on the web page format.

4. The computerized method of claim 1, wherein the metadata is category data.

5. A machine readable medium comprising:

receiving a web page from a community-generated web site, the web page associated with a particular piece of content;

extracting a plurality of terms from the web page;

adding the plurality of terms to content metadata associated with the piece of content;

extracting specific category data from the content metadata;

loading the specific category data into a category dataset; and

reducing a dimensionality of the category dataset based on the category dataset and relation data, wherein the relation data defines a relationship between the category dataset and the content associated with the category dataset.

6. The machine readable medium of claim 5, wherein extracting the plurality of terms further comprises at least one of stemming the terms in the web page, removing the stop terms from the web page, and extracting a limited number of terms from the web page.

7. The machine readable medium of claim 5, wherein extracting the plurality of terms further comprises defining parser actions on the web page format.

8. The machine readable medium of claim 5, wherein the metadata is category data.

9. An apparatus comprising:

means for receiving a web page from a community-generated web site, the web page associated with a particular piece of content;

means for extracting a plurality of terms from the web page;

means for adding the plurality of terms to content metadata associated with the piece of content;

means for extracting specific category data from the content metadata;

means for loading the specific category data into a category dataset; and

means for reducing a dimensionality of the category dataset based on the category dataset and relation data, wherein the relation data defines a relationship between the category dataset and the content associated with the category dataset.

10. The apparatus of claim 9, wherein the means for extracting the plurality of terms further comprises at least one of stemming the terms in the web page, removing the stop terms from the web page, and extracting a limited number of terms from the web page.

11. The apparatus of claim 9, wherein the means for extracting the plurality of terms further comprises defining parser actions on the web page format.

12. The apparatus of claim 9, wherein the metadata is category data.

13. A system comprising:

a processor;

a memory coupled to the processor though a bus; and

a process executed from the memory by the processor to cause the processor to receive a web page from a community-generated web site, the web page associated with a particular piece of content, to extract a plurality of terms from the web page, to add the plurality of terms to content metadata associated with the piece of content, to extract specific category data from the content metadata, to load the specific category data into a category dataset, and reducing a dimensionality of the category dataset based on the category dataset and relation data, wherein the relation data defines a relationship between the category dataset and the content associated with the category dataset.

14. The system of claim 13, wherein extracting the plurality of terms further comprises at least one of stemming the terms in the web page, removing the stop terms from the web page, and extracting a limited number of terms from the web page.

15. The system of claim 13, wherein extracting the plurality of terms further comprises defining parser actions on the web page format.

16. The system of claim 13, wherein the metadata is category data.