Method and apparatus for generating metadata for a document

• US 20020016800A1
• Filed: 03/23/2001
• Published: 02/07/2002
• Est. Priority Date: 03/27/2000
• Status: Abandoned Application

• Current Assignee: EMC Corporation (Dell Technologies Inc.)
• Original Assignee: EMC Corporation (Dell Technologies Inc.)
• Inventors: Shanahan, Matthew Raymond, Rankov, Alex, Spivak, Victor, Shao, Howard I-Hui, Abnous, Razmik
• Application Number:
  US09/815,591
• Publication Number:
  US 20020016800A1
• Time in Patent Office: Days
• US Class Current: 715/523
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/38 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

A method and system of generating metadata for a document so that the document may be identified by a subsequent search. A conceptual model is generated for the document, wherein the conceptual model indicates one or more concepts that are recognized in the document. A concept is defined by a plurality of features, each feature being associated with a feature weight. By referencing the conceptual model, one or more auto-attributes may be assigned to the document. Also, by referencing the conceptual model, the document may be categorized to one or more categories of a categorization taxonomy by assigning one or more auto-categories. The generated metadata, including the conceptual model, the one or more auto-attributes, and the one or more auto-categories, may be stored in a memory so that the subsequent search may identify the document by examining the generated metadata.

Claims

1. A computer-implemented method of processing a document, said method comprising:

converting a document into a common format document;

recognizing a concept in said common format document, wherein said concept represents a basic idea expressed in said common format document; and

incorporating said concept in a conceptual model.

2. The computer-implemented method of claim 1, wherein recognizing said concept includes:

identifying a plurality of features in said common format document, wherein said plurality of features represents evidence of said concept in said common format document.

3. The computer-implemented method of claim 2, wherein recognizing said concept further includes:

calculating a concept weight for said concept using a plurality of feature weights associated with said plurality of features, wherein said concept weight represents a recognition confidence level for said concept; and

comparing said concept weight with a predetermined threshold value.

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

by referencing said conceptual model, generating an auto-attribute, said auto-attribute being a descriptive label for said common format document.

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

by referencing said conceptual model, assigning said common format document to a subject category.

6. The computer-implemented method of claim 1, wherein said converting includes converting said document into a common format document that is in an XML format.

7. A computer-readable medium to direct a computer to function in a specified manner, comprising:

instructions to recognize a basic idea expressed in a document;

instructions to assign a concept identification to said basic idea; and

instructions to generate a conceptual model based upon said concept identification.

8. The computer-readable medium of claim 7, wherein said instructions to recognize said basic idea include:

instructions to determine whether a plurality of features is present in said document, wherein said plurality of features represents evidence that said basic idea is expressed in said document.

9. The computer-readable medium of claim 8, wherein said instructions to recognize said basic idea further include:

instructions to calculate a recognition confidence level for said basic idea using a plurality of feature weights associated with said plurality of features; and

instructions to compare said recognition confidence level with a predetermined threshold value.

10. The computer-readable medium of claim 9, wherein said instructions to generate said conceptual model include:

instructions to incorporate said recognition confidence level in said conceptual model.

11. The computer-readable medium of claim 7, further comprising:

instructions to assign an auto-attribute to said document based upon said conceptual model, wherein said auto-attribute represents a descriptive label for said document.

12. The computer-readable medium of claim 7, further comprising:

instructions to place said document in a category of a categorization taxonomy based upon said conceptual model, wherein said categorization taxonomy includes a plurality of categories.

13. The computer-readable medium of claim 12, wherein said instructions to place said document in said category include:

instructions to assign an auto-category to said document, wherein said auto-category represents a descriptive label for said category.

15. The computer of claim 14, wherein said memory further includes:

a document integration module, said document integration module having;

a third module configured to direct said processor to convert an initial format document to said document, which has a common format.

16. The computer of claim 15, wherein said document integration module further has:

a fourth module configured to direct said processor to separate a text portion from said initial format document; and

a fifth module configured to direct said processor to incorporate said text portion in said document.

17. The computer of claim 14, wherein said first module has:

a sixth module configured to direct said processor to determine whether a plurality of features is present in said document, wherein said plurality of features represents evidence of said concept in said document;

a seventh module configured to direct said processor to calculate a concept weight for said concept using a plurality of feature weights associated with said plurality of features, wherein said concept weight represents a recognition confidence level for said concept; and

an eighth module configured to direct said processor to compare said concept weight with a predetermined threshold value.

18. The computer of claim 14, wherein said memory further includes:

a modeling directory,

and wherein said document modeling module further has;

a ninth module configured to direct said processor to store said conceptual model in said modeling directory.

19. The computer of claim 14, wherein said document modeling module further has:

a tenth module configured to direct said processor to generate an auto-attribute based upon said conceptual model, wherein said auto-attribute represents a descriptive label for said document.

20. The computer of claim 14, wherein said document modeling module further has:

an eleventh module configured to direct said processor to categorize said document in a category of a plurality of categories based upon said conceptual model.

14. A computer, comprising:

a processor; and

a memory connected to said processor, wherein said memory includes;

a document modeling module, said document modeling module having;

a first module configured to direct said processor to recognize a concept in a document, wherein said concept represents a basic idea expressed in said document; and

a second module configured to direct said processor to generate a conceptual model based upon said concept.