Knowledge neighborhoods

• US 20040111386A1
• Filed: 01/08/2001
• Published: 06/10/2004
• Est. Priority Date: 01/08/2001
• Status: Abandoned Application

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Gilmour, David L., Goldberg, Jonathan M.
• Application Number:
  US09/754,385
• Publication Number:
  US 20040111386A1
• Time in Patent Office: Days
• US Class Current: 707/1
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957

Abstract

Knowledge neighborhoods are generated from concepts associated with knowledge profiles within an organization to create a knowledge taxonomy. A root concept is used to select a set of profiles associated with the root concept. At least one concept common to the set of profiles is determined and an affinity between the common concept and the root concept is derived. The common concept is a knowledge neighbor of the root concept. A set of one or more such knowledge neighbors forms one level of the knowledge neighborhood for the root concept. The knowledge neighborhood can grow through various levels by using one or more of the common concepts as a new root concept. A knowledge map can be employed to graphically illustrate the knowledge neighborhood.

Claims

1. A method of generating a knowledge neighborhood comprising:

selecting a set of knowledge profiles associated with a root concept;

determining a knowledge neighbor for the root concept, the knowledge neighbor being a concept common to the selected knowledge profiles; and

deriving an affinity for the knowledge neighbor to represent a relationship between the root concept and the knowledge neighbor.

2. The method of claim 1 further comprising:

using the knowledge neighbor as a new root concept to determine an additional knowledge neighbor.

3. The method of claim 1, wherein determining a knowledge neighbor comprises:

filtering all concepts common to the selected knowledge profiles against a pre-determined confidence level threshold.

4. The method of claim 1, wherein selecting the set of knowledge profiles comprises:

filtering all knowledge profiles associated with the root concept against a pre-determined confidence level threshold.

5. The method of claim 1 further comprising:

obtaining an identity for the root concept.

6. The method of claim 1, wherein obtaining the identity for the root concept comprises:

receiving a user selection of the root concept.

7. The method of claim 1, wherein the root concept is selected from the group consisting of a knowledge term, a profile, a search criteria, and a document.

8. The method of claim 1 further comprising:

creating a knowledge map to graphically illustrate the root concept, the knowledge neighbor, and the affinity.

9. The method of claim 8 further comprising:

using the knowledge map to designate the knowledge neighbor as a new root concept to determine an additional knowledge neighbor.

10. The method of claim 8 further comprising:

overlaying the knowledge map on an earlier generated knowledge map.

11. The method of claim 8 further comprising:

graphically illustrating more than one knowledge neighbor as a single knowledge neighbor.

12. The method of claim 8, wherein creating the knowledge map comprises:

graphically illustrating the knowledge neighbor if it satisfies an affinity threshold.

13. The method of claim 8, wherein the knowledge map is a directed graph comprising:

a node representing the root concept;

a node representing the knowledge neighbor; and

an edge representing the affinity, the edge graphically linking the node representing the root concept and the node representing the knowledge neighbor.

14. The method of claim 13, wherein the edge is illustrated with a length proportional to the affinity.

15. The method of claim 13, wherein the edge is illustrated with a color assigned to the affinity.

16. The method of claim 1, wherein deriving the affinity comprises:

counting the knowledge profiles associated with the knowledge neighbor; and

calculating the affinity using the count of the knowledge profiles.

17. The method of claim 16, wherein calculating the affinity comprises:

factoring in a confidence level for the knowledge neighbor in each of the counted knowledge profiles.

18. The method of claim 1, wherein deriving the affinity comprises using a formula

19. A computer-readable medium having computer-executable instructions comprising:

selecting a set of knowledge profiles associated with a root concept;

determining a knowledge neighbor for the root concept, the knowledge neighbor being a concept common to the selected knowledge profiles; and

deriving an affinity for the knowledge neighbor to represent a relationship between the root concept and the knowledge neighbor.

20. The computer-readable medium of claim 19 having further instructions comprising:

using the knowledge neighbor as a new root concept to determine an additional knowledge neighbor.

21. The computer-readable medium of claim 19 having further instructions comprising:

obtaining an identity for the root concept.

22. The computer-readable medium of claim 19 having further instructions comprising:

creating a knowledge map to graphically illustrate the root concept, the knowledge neighbor, and the affinity.

23. The computer-readable medium of claim 22 having further instructions comprising:

using the knowledge map to designate the knowledge neighbor as a new root concept to determine an additional knowledge neighbor.

24. The computer-readable medium of claim 22 having further instructions comprising:

overlaying the knowledge map on an earlier generated knowledge map for the root concept.

25. The computer-readable medium of claim 22 having further instructions comprising:

graphically illustrating more than one knowledge neighbor as a single knowledge neighbor.

26. A computer system comprising:

a processing unit;

a memory coupled to the processing unit through a bus;

a computer-readable medium coupled to the processing unit through the bus; and

a knowledge neighborhood generation process executed from the computer-readable medium to cause the processing unit to select a set of knowledge profiles associated with a root concept, determine a knowledge neighbor for the root concept from the selected knowledge profiles, and derive an affinity for the knowledge neighbor.

27. The computer system of claim 26, wherein the knowledge neighborhood generation process further causes the processing unit to use the knowledge neighbor as a new root concept to determine an additional knowledge neighbor.

28. The computer system of claim 26, wherein the knowledge neighborhood generation process further causes the processing unit to obtain an identity for the root concept.

29. The computer system of claim 26 further comprising:

a knowledge mapping process executed from the computer-readable medium to cause the processing unit to graphically illustrate the knowledge neighbor and the affinity as a knowledge neighborhood for the root concept.

30. The computer system of claim 29, wherein the knowledge mapping process further causes the processing unit to graphically overlay the knowledge neighborhood on an earlier generated knowledge neighborhood for the root concept.