SEMANTIC CLASSIFICATION OF VARIABLE DATA CAMPAIGN INFORMATION

• US 20120046937A1
• Filed: 08/17/2010
• Published: 02/23/2012
• Est. Priority Date: 08/17/2010
• Status: Abandoned Application

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Ocke, Kirk J., Gaucas, Dale Ellen, Shepherd, Michael David, Gombert, Barry Glynn
• Application Number:
  US12/857,997
• Publication Number:
  US 20120046937A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 40/174 Form filling; Merging
• CPC Class Codes: G06F 40/18 of spreadsheets form-filling G06F40/174

Abstract

A method and system for semantically classifying variable data campaign information. The method and system include loading, by a processing device, a variable data campaign from a computer readable storage medium operably connected to the processing device; extracting, by the processing device, variable data from the campaign; semantically classifying, by the processing device, the variable data to produce semantically classified variable data; building, by the processing device, a variable data campaign model based upon the semantically classified variable data; and storing, by the processing device, the variable data campaign model in the computer readable storage medium.

Claims

1. A method of semantically classifying variable data campaign information comprising:

loading, by a processing device, a variable data campaign from a computer readable storage medium operably connected to the processing device;

extracting, by the processing device, variable data from the campaign;

semantically classifying, by the processing device, the variable data to produce semantically classified variable data;

building, by the processing device, a variable data campaign model based upon the semantically classified variable data; and

storing, by the processing device, the variable data campaign model in the computer readable storage medium.

2. The method of claim 1, wherein the extracting variable data from the campaign comprises:

extracting variable data campaign data, wherein the variable data campaign data includes a plurality of variable data field;

determining a data source for each of the variable data fields;

extracting a list of values associated with each variable data field based upon the determined data source; and

extracting meta-data related to the variable data field.

3. The method of claim 2, wherein the determined data source includes at least one of a database, a spread sheet, and a linked list.

4. The method of claim 1, wherein the semantically classifying the variable data comprises applying one or more classification techniques to the variable data to determine one or more semantic classifications for each variable data field and whether the one or more semantic classifications of each first variable data field has an ancestor/descendent relationship.

5. The method of claim 4, wherein the one or more classification techniques includes at least one of:

mapping a variable data field to one or more semantic elements based upon the variable data field type associated with the variable data field;

inferring one or more semantic elements to which a variable data field is mapped to based upon the name of the variable data field;

using a list of values associated with a variable data field to determine a semantic element to map the variable data field to; and

determining a name based upon a data source of one of a variable data field.

6. The method of claim 4, wherein the determining whether the one or more semantic classifications of a variable data field in the variable data has an ancestor/descendent relationship comprises:

determining a source of content for the variable data field;

applying at least one heuristic to the content;

determining whether the result of the at least one heuristic indicates an ancestor/descendent relationship for the variable data field; and

determining at least one specific classification for the variable data field based upon the result.

7. The method of claim 6, wherein the at least one specific classification includes at least one of text or image.

8. The method of claim 1, wherein the variable data campaign model comprises a hierarchal organization of one or more variable data fields extracted from the variable data campaign.

9. A system for semantically classifying variable data campaign information comprising:

a processing device; and

a computer readable storage medium in communication with the processing device,

wherein the computer readable medium comprises one or more programming instructions for;

loading, by the processing device, a variable data campaign from the computer readable storage medium operably connected to the processing device,

extracting, by the processing device, variable data from the campaign,

semantically classifying, by the processing device, the variable data to produce semantically classified variable data,

building, by the processing device, a variable data campaign model based upon the semantically classified variable data, and

storing, by the processing device, the variable data campaign model in the computer readable storage medium.

10. The system of claim 9, wherein the one or more programming instructions for extracting any variable data from the campaign comprise one or more programming instructions for:

extracting variable data campaign data, wherein the variable data campaign data includes a plurality of variable data field;

determining a data source for each of the variable data fields;

extracting a list of values associated with each variable data field based upon the determined data source; and

extracting meta-data related to the variable data field.

11. The system of claim 10, wherein the determined data source includes at least one of a database, a spread sheet, and a linked list.

12. The system of claim 9, wherein the one or more programming instructions for semantically classifying the variable data comprises applying one or more classification techniques to the variable data to determine one or more semantic classifications for each variable data field and whether the one or more semantic classifications of each variable data field has an ancestor/descendent relationship.

13. The system of claim 12, wherein the one or more programming instructions for applying one or more classification techniques comprise one or more programming instructions for:

mapping a variable data field to one or more semantic elements based upon the variable data field type associated with the variable data field;

inferring one or more semantic elements to which a variable data field is mapped to based upon the name of the variable data field;

using a list of values associated with a variable data field to determine a semantic element to map the variable data field to; and

determining a name based upon a data source of one of a variable data field.

14. The system of claim 12, wherein one or more programming instructions for determining whether the one or more semantic classifications of a variable data field has an ancestor/descendent relationship comprises:

determining a source of content for the variable data field;

applying at least one heuristic to the content;

determining whether the result of the at least one heuristic indicates an ancestor/descendent relationship for the variable data field; and

determining at least one specific classification for the variable data field based upon the result.

15. The system of claim 14, wherein the at least one specific classification includes at least one of text or image.

16. The system of claim 9, wherein the variable data campaign model comprises a hierarchal organization of one or more data fields extracted from the variable data campaign.

17. A method of semantically classifying variable data campaign information comprising:

loading, by a processing device, a variable data campaign from a computer readable memory operably connected to the processing device;

extracting, by the processing device, variable data from the campaign, wherein the variable data comprises variable data fields and any related values and attributes;

semantically classifying, by the processing device, the variable data according to at least one classification technique such that each identified variable data field is mapped to at least one semantic element;

generating, by the processing device, a variable data campaign model based upon the identified variable data and the mapped semantic elements; and

storing, by the processing device, the variable data campaign model in the computer readable medium.

18. The method of claim 17, wherein the extracting variable data from the campaign comprises:

extracting variable data campaign data, wherein the variable data campaign data includes a plurality of variable data field;

determining a data source for each of the variable data fields;

extracting a list of values associated with each variable data field based upon the determined data source; and

extracting meta-data related to the variable data field.

19. The method of claim 18, wherein the determined data source includes at least one of a database, a spread sheet, and a linked list.

20. The method of claim 17, wherein the variable data campaign model comprises a hierarchal organization of one or more data fields extracted from the variable data campaign.