System, method and computer program for faceted classification synthesis

• US 7,849,090 B2
• Filed: 01/22/2007
• Issued: 12/07/2010
• Est. Priority Date: 03/30/2005
• Status: Active Grant

• Current Assignee: Primal Fusion Inc.
• Original Assignee: Primal Fusion Inc.
• Inventors: Sweeney, Peter
• Primary Examiner(s): Cottingham, John R.
• Assistant Examiner(s): Allen, Nicholas E
• Application Number:
  US11/625,452
• Publication Number:
  US 20070118542A1
• Time in Patent Office: 1,415 Days
• Field of Search: 707/741
• US Class Current: 707/741
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 16/84 Mapping; Conversion
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

A method (system and computer program product) performs facet classification synthesis to relate concepts represented by concept definitions defined in accordance with a faceted data set comprising facets, facet attributes, and facet attribute hierarchies. Dimensional concept relationships are expressed between the concept definitions. Two concept definitions are determined to be related in a particular dimensional concept relationship by examining whether at least one of explicit relationships and implicit relationships exist in the faceted data set between the respective facet attributes of the two concept definitions.

Claims

1. A method for performing synthesis of relationships between a plurality of concept definitions using a domain of information, wherein the domain of information is a faceted domain that comprises a plurality of facets each having a plurality of facet attributes, wherein each of the concept definitions comprises at least one of the plurality of facet attributes, and wherein the method comprises:

determining whether any explicit relationships exist between the plurality of concept definitions, wherein an explicit relationship is determined to exist between any two of the plurality concept definitions if the two of the plurality of concept definitions share at least one common facet attribute or each has a facet attribute of the same lineage in at least one facet attribute hierarchy of the plurality of facet attributes;

determining whether any implicit relationships exist between the plurality of concept definitions, wherein an implicit relationship between two of the plurality of concept definitions is determined to exist based on a statistical identification of a relationship between a facet attribute in a first of the two of the plurality of concept definitions and a facet attribute in a second of the two of the plurality of concept definitions; and

when it is determined that at least one explicit relationship and/or at least one implicit relationship exists between two of the plurality of concept definitions, synthesizing a relationship between the two of the plurality of concept definitions.

2. The method of claim 1, further comprising:

creating a dimensional concept hierarchy, based on the synthesized relationships between concept definitions.

3. The method of claim 2, wherein the act of synthesizing the relationship between the two concept definitions further comprises:

determining a dimensional axis between the two concept definitions.

4. The method of claim 3, further comprising:

creating at least one dimensional concept hierarchy, based, at least in part, on the dimensional axis.

5. The method of claim 3, further comprising selecting one or more facet attributes for defining said dimensional axis, said selecting in accordance with at least one of:

the respective priorities of the facet attributes in the at least one facet attribute hierarchy; and

a particular concept definition comprising the one or more facet attributes to ascribe a particular meaning to said dimensional axis.

6. The method of claim 2, further comprising:

evaluating sets of concept relationships for the presence of indirect relationships and assembling the dimensional concept hierarchy without the indirect relationships.

7. The method of claim 2, further comprising:

determining a priority for the concept definitions in the dimensional concept hierarchy based, at least in part, on at least one of priorities of facet attributes in the at least one facet attribute hierarchy and a count of the number of facet attributes in the respective concept definitions.

8. The method of claim 2, wherein the domain of information comprises a plurality content nodes, and wherein the act of creating a dimensional concept taxonomy further comprises:

assigning facet attributes to the plurality of content nodes of the domain of information.

9. The method of claim 1, wherein an explicit or implicit relationship is determined to exist between any two of the plurality of concept definitions if all of the facet attributes of a one of the two concept definitions are related to all or a subset of the facet attributes of the other of the two concept definitions.

10. The method of claim 1 wherein the plurality of concept definitions are preliminary concept definitions, wherein relationships between the preliminary concept definitions are represented in an input concept hierarchy and wherein the method comprises:

revising the preliminary concept definitions by extending the preliminary concept definitions with additional facet attributes in response to whether any two relationships in the input concept hierarchy may be related, directly or indirectly, in a dimensional concept relationship by at least one of explicit relationships and implicit relationships.

11. The method of claim 1, further comprising:

for the domain of information creating a keyword hierarchy comprising unique keywords as representative of facet attributes in the domain.

12. The method of claim 11, further comprising:

defining a dimensional concept taxonomy for the domain of information, wherein the domain of information comprises a plurality of content nodes, wherein each of the plurality of content nodes is associated with one or more concepts represented by the concept definitions, said taxonomy comprising a hierarchy of dimensional concept relationships generated from the concept definitions and the keyword hierarchy, wherein each concept definition is mapped to one or more representative keywords of the keyword hierarchy.

13. The method of claim 1, further comprising:

examining content nodes for their respective one or more concept definitions.

14. The method of claim 13, wherein the act of examining content nodes further comprises:

processing a localized region of the domain using content nodes proximal to a selected content node.

15. A computer system comprising:

at least one memory that stores processor-executable instructions for performing faceted classification synthesis of relationships between a plurality of concept definitions using a domain of information comprising a plurality of facets each having a plurality of facet attributes, wherein each of the concept definitions comprises at least one of the plurality of facet attributes; and

at least one hardware processor, coupled to the at least one memory that executes the instructions to;

determine whether any explicit relationships exist between the plurality of concept definitions, wherein an explicit relationship is determined to exist between any two of the plurality concept definitions if the two of the plurality of concept definitions share at least one common facet attribute or each has a facet attribute of the same lineage in at least one facet attribute hierarchy of the plurality of facet attributes;

determine whether any implicit relationships exist between the plurality of concept definitions, wherein an implicit relationship between two of the plurality of concept definitions is determined to exist based on a statistical identification of a relationship between a facet attribute in a first of the two of the plurality of concept definitions and a facet attribute in a second of the two of the plurality of concept definitions; and

when it is determined that at least one explicit relationship and/or at least one implicit relationship exists between two of the plurality of concept definitions, synthesize a relationship between the two of the plurality of concept definitions.

16. The computer system of claim 15, wherein the at least one processor executes the instructions to:

create a dimensional concept hierarchy, based on the synthesized relationships between concept definitions.

17. The computer system of claim 16, wherein the at least one processor is configured to:

in response to determining that two concept definitions are related, determine a dimensional axis between the two concept definitions.

18. The computer system of claim 17, wherein the at least one processor executes the instructions to:

create at least one dimensional concept hierarchy, based on the dimensional axis.

19. The computer system of claim 17, wherein the at least one processor executes the instructions to:

select one or more facet attributes for defining said dimensional axis, said selecting in accordance with at least one of;

the respective priorities of the facet attributes in the at least one facet attribute hierarchy; and

a particular concept definition comprising the one or more facet attributes to ascribe a particular meaning to said dimensional axis.

20. The computer system of claim 16, wherein the at least one processor executes instructions to evaluate sets of dimensional concept relationships for the presence of indirect relationships and assembling the dimensional concept hierarchy without the indirect relationships.

21. The computer system of claim 16, wherein the at least one processor executes the instructions to:

determine a priority for the concept definitions in the dimensional concept hierarchy based on at least one of priorities of facet attributes in the at least one facet attribute hierarchy and a count of the number of facet attributes in the respective concept definitions.

22. The computer system of claim 16, wherein the domain of information comprises a plurality of content nodes and wherein the at least one processor creates the dimensional concept taxonomy by:

assigning facet attributes to the plurality of content nodes of the domain of information.

23. The computer system of claim 15, wherein an explicit or implicit relationship is determined to exist between any two of the plurality of concept definitions if all of the facet attributes of a one of the two concept definitions are related to all or a subset of the facet attributes of the other of the two concept definitions.

24. The computer system of claim 15, wherein the plurality of concept definitions are preliminary concept definitions, wherein relationships between the preliminary concept definitions are represented in an input concept hierarchy and wherein the at least one processor executes the instructions to:

revise said preliminary concept definitions by extending the preliminary concept definitions with additional facet attributes in response to whether any two relationships in the input concept hierarchy may be related, directly or indirectly, in a dimensional concept relationship by at least one of explicit relationships and implicit relationships.

25. The computer system of claim 15, wherein the at least one processor executes the instructions to create a keyword hierarchy comprising unique keywords as representative of facet attributes in the domain.

26. The computer system of claim 25 wherein the at least one processor executes the instructions to:

define a dimensional concept taxonomy for a the domain of information, wherein the domain of information comprises a plurality of content nodes, wherein each of the plurality of content nodes is associated with one or more concepts represented by the concept definitions, said taxonomy comprising a hierarchy of dimensional concept relationships generated from the concept definitions and the keyword hierarchy, wherein each concept definition is mapped to one or more representative keywords of the keyword hierarchy.

27. The computer system of claim 15, wherein the at least one processor executes the instructions to:

examine content nodes for their respective one or more concept definitions.

28. The computer system of claim 27, wherein at least one processor examines content nodes by processing a localized region of the domain using content nodes proximal to a selected content node.

29. A computer program storage product storing instructions that when executed on a computer system, perform a method for performing synthesis of relationships between a plurality of concept definitions using a domain of information, wherein the domain of information comprises a plurality of facets each having a plurality of facet attributes, wherein each of the plurality of concept definitions comprises at least one the plurality of facet attributes, and wherein the method comprises:

determining whether any explicit relationships exist between the plurality of concept definitions, wherein an explicit relationship is determined to exist between any two of the plurality concept definitions if the two of the plurality of concept definitions share at least one common facet attribute or each has a facet attribute of the same lineage in at least one facet attribute hierarchy of the plurality of facet attributes;

determining whether any implicit relationships exist between the plurality of concept definitions, wherein an implicit relationship between two of the plurality of concept definitions is determined to exist based on a statistical identification of a relationship between a facet attribute in a first of the two of the plurality of concept definitions and a facet attribute in a second of the two of the plurality of concept definitions; and

when it is determined that at least one explicit relationship and/or at least one implicit relationship exists between two of the plurality of concept definitions, synthesizing a relationship between the two of the plurality of concept definitions.

30. The computer program storage product of claim 29, wherein the method further comprises creating a dimensional concept hierarchy, based on the synthesized relationships between concept definitions.

31. The computer program storage product of claim 30, wherein the act of synthesizing the relationship between the two concept definitions further comprises:

determining a dimensional axis between the two concept definitions.

32. The computer program storage product of claim 31, wherein the method further comprises:

creating at least one dimensional concept hierarchy, based, at least in part, on the dimensional axis.

33. The computer program storage product of claim 29, wherein an explicit or implicit relationship is determined to exist between any two of the plurality of concept definitions if:

all of the facet attributes of a one of the two concept definitions are related to all or a subset of the facet attributes of the other of the two concept definitions.

34. The computer program storage product of claim 31 wherein the method further comprises selecting one or more facet attributes for defining said dimensional axis, said selecting in accordance with at least one of:

the respective priorities of the facet attributes in the morpheme at least one facet attribute hierarchy; and

a particular concept definition comprising the one or more facet attributes to ascribe a particular meaning to said dimensional axis.

35. The computer program storage product of claim 30, wherein the method further comprises evaluating sets of dimensional concept relationships for the presence of indirect relationships and assembling the dimensional concept hierarchy without the indirect relationships.

36. The computer program storage product of claim 30, wherein the method further comprises:

determining a priority for the concept definitions in the dimensional concept hierarchy based, at least in part, on at least one of priorities of facet attributes in the at least one facet attribute hierarchy and a count of the number of facet attributes in the respective concept definitions.

37. The computer program storage product of claim 30, wherein the domain of information comprises a plurality content nodes, and wherein the act of creating a dimensional concept taxonomy further comprises:

assigning facet attributes to the plurality of content nodes of the domain of information.

38. The computer program storage product of claim 29, wherein the plurality of concept definitions are preliminary concept definitions, wherein relationships between the preliminary concept definitions are represented in an input concept hierarchy and wherein the method comprises:

revising the preliminary concept definitions by extending the preliminary concept definitions with additional facet attributes in response to whether any two relationships in the input concept hierarchy may be related, directly or indirectly, in a dimensional concept relationship by at least one of explicit relationships and implicit relationships.

39. The computer program storage product of claim 29, wherein the method further comprises, for the domain of information creating a keyword hierarchy comprising unique keywords as representative of facet attributes in the domain.

40. The computer program storage product of claim 39, wherein the method further comprises:

defining a dimensional concept taxonomy for the domain of information, wherein the domain of information comprises a plurality of content nodes, wherein each of the plurality of content nodes is associated with one or more concepts represented by the concept definitions, said taxonomy comprising a hierarchy of dimensional concept relationships generated from the concept definitions and the keyword hierarchy, wherein each concept definition is mapped to one or more representative keywords of the keyword hierarchy.

41. The computer program storage product of claim 29, wherein the method further comprises:

examining content nodes for their respective one or more concept definitions.

42. The computer program storage product of claim 41, wherein the act of examining content nodes further comprises:

processing a localized region of the domain using content nodes proximal to a selected content node.