Concept-based searching of unstructured objects

• US 7,890,514 B1
• Filed: 12/05/2005
• Issued: 02/15/2011
• Est. Priority Date: 05/07/2001
• Status: Expired due to Term

• Current Assignee: CONQ Incorporated
• Original Assignee: IxReveal Incorporated
• Inventors: Sha, David D., Mohan, Usha, Mohan, Rengaswamy
• Primary Examiner(s): Pham; Khanh B
• Assistant Examiner(s): CHEEMA, AZAM M
• Application Number:
  US11/275,046
• Time in Patent Office: 1,898 Days
• Field of Search: None
• US Class Current: 707/748
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/30 of unstructured textual data document management systems G06F16/93
• CPC Class Codes: Y10S 707/956 Hierarchical
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99944 Object-oriented database structure
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A method, operating model, system, data structure, computer program and computer program product for analyzing and categorizing and exploring or querying unstructured information and for tracking trends and exceptions. Method for analytical processing of unstructured objects in a dimensional space. Method for tracking trends in concepts. Method for tracking exceptions in concepts. Tools and interface for displaying concepts, query results, tracked trends and exceptions.

Claims

1. A computer-readable storage medium, comprising code representing instructions to cause a processor to:

identify a plurality of concepts present in an unstructured object present in a corpus of unstructured objects;

define a Gaussian distribution representing a number of occurrences of each concept in the plurality of concepts present in the unstructured object;

calculate a weighted value for a first concept from the plurality of concepts, the weighted value being based at least in part on at least one of;

a number of occurrences of the first concept in the unstructured object;

a ratio of a number of categories in which the first concept occurs to a total number of all categories;

a ratio of a frequency of occurrence of the first concept in the unstructured object to a frequency of occurrence of the first concept in the corpus;

or

a ratio of a number of occurrences of the first concept in the unstructured object to a total number of all concepts, including the plurality of concepts, that occur in the unstructured object;

determine that the weighted value is greater than a first threshold value and less than a second threshold value, the first threshold value being five or fewer standard deviations below a mean weighted value of the Gaussian distribution, the second threshold value being five or fewer standard deviations above the mean weighted value of the Gaussian distribution; and

identify the first concept as a key concept associated with the unstructured object, the key concept representing a meaning of the unstructured object.

2. The computer-readable storage medium of claim 1, further comprising code to define a reduced dimensionality vector, the reduced dimensionality vector including a dimension having a dimension value corresponding to the first concept, the dimension value being calculated based at least in part on a number of occurrences of the first concept in the unstructured object.

3. The computer-readable storage medium of claim 2, wherein the dimension of the reduced dimensionality vector is a first dimension and the dimension value is a first dimension value, the reduced dimensionality vector including at least a second dimension, the second dimension having a second dimension value, corresponding to a second concept, related to the first concept, in the unstructured object, the code to define the reduced dimensionality vector including code to combine at least the first dimension and the second dimension, the combining including calculating a vector value for the reduced dimensionality vector based at least in part on the first dimension value and the second dimension value.

4. The compute-readable storage medium of claim 2, wherein the weighted value is based at least in part on the dimension value.

5. The computer-readable storage medium of claim 3, wherein the weighted value is based at least in part on the vector value.

6. The computer-readable storage medium of claim 4, wherein the code to identify a category is based at least in part on a ratio of a number of categories in which the first concept occurs to a total number of all categories.

7. The computer-readable storage medium of claim 4, wherein the code to identify a category is based at least in part on a ratio of a number of occurrences of the first concept in the unstructured object to a total number of all concepts that occur in the unstructured object.

8. The computer-readable storage medium of claim 1, further comprising code representing instructions to cause a processor to:

identify a category associated with the unstructured object.

9. The computer-readable storage medium of claim 1, further comprising code to identify at least a second concept associated with the first concept, the second concept being present in the unstructured object.

10. The computer-readable storage medium of claim 1, further comprising code to receive user input including one or more additional concepts associated with the first concept.

11. A computer-readable storage medium, comprising code representing instructions to cause a processor to:

identify a plurality of concepts present in an unstructured object present in a corpus of unstructured objects;

define a reduced dimensionality vector representation for the unstructured object, the reduced dimensionality vector representation including at least one dimension corresponding to a seed concept from the plurality of concepts and a second concept from the plurality of concepts, the second concept being related to the seed concept, the at least one dimension having a dimension value based at least in part on a number of occurrences of the seed concept in the unstructured object;

calculate a weighted value for the at least one dimension, the weighted value being based at least in part on the dimension value;

define a Gaussian distribution representing a number of occurrences of each concept from the plurality of concepts present in the unstructured object;

determine that the weighted value is greater than a first threshold value and less than a second threshold value, the first threshold value being a five or fewer standard deviations below a mean weighted value of the Gaussian distribution, the second threshold value being five or fewer standard deviations above the mean weighted value of the Gaussian distribution; and

identify the seed concept as a key concept associated with the unstructured object, the key concept representing a theme of the unstructured object.

12. The computer-readable storage medium of claim 11, wherein the weighted value is further based at least in part on at least one of:

a number of occurrences of the seed concept in the unstructured object;

a ratio of a number of categories in which the seed concept occurs to a total number of all categories;

a ratio of a frequency of occurrence of the seed concept in the unstructured object to a frequency of occurrence of the seed concept in the corpus;

or

a ratio of a number of occurrences of the seed concept in the unstructured object to a total number of all concepts, including the plurality of concepts, that occur in the unstructured object.

13. The computer-readable storage medium of claim 11, further comprising code representing instructions to cause a processor to:

identify a category associated with the unstructured object.

14. The computer-readable storage medium of claim 13, wherein the code to identify a category is based at least in part on a ratio of a number of categories in which the seed concept occurs to a total number of all categories.

15. The computer-readable storage medium of claim 13, wherein the code to identify a category is based at least in part on a ratio of a number of occurrences of the seed concept in the unstructured object to a total number of all concepts, including the plurality of concepts, that occur in the unstructured object.

16. The computer-readable storage medium of claim 11, further comprising code to receive user input including one or more additional concepts associated with the seed concept.

17. The computer-readable storage medium of claim 11, further comprising code representing instructions to cause a processor to:

receive a query;

determine that both the query and the unstructured object include the key concept; and

send a query result in response to the query, the query result including an indication of the unstructured object.

18. The computer-readable storage medium of claim 11, further comprising code representing instructions to cause a processor to:

receive a query;

determine that both the query and the unstructured object include the key concept; and

send a query result in response to the query, the query result including an indication of the unstructured object.