System and method of self-learning conceptual mapping to organize and interpret data

• US 20050251383A1
• Filed: 05/10/2005
• Published: 11/10/2005
• Est. Priority Date: 05/10/2004
• Status: Active Grant

• Current Assignee: KinetX, Inc.
• Original Assignee: KinetX, Inc.
• Inventors: Murray, Jonathan
• Granted Patent: US 7,447,665 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

In a computer implemented method of researching textual data sources, textual data is reduced to a plurality of distinctive words based on frequency of usage within the textual data. The distinctive words are converted into first numeric representations of vectors containing random numbers. A first self-organizing map is formed from the first numeric representations and organized by similarities between the vectors. A second self-organizing map is formed from second numeric representations generated from the organization of the first self-organizing map. The second numeric representations are vectors derived from the first self-organizing map. The vectors are used to train the second self-organizing map. The vectors derived from the first self-organizing map are organized into clusters of similarities between the vectors on the second self-organizing map. Dialectic arguments are formed from the second self-organizing map to interpret the textual data.

Claims

1. A computer implemented method of researching textual data sources, comprising:

converting textual data into first numeric representations;

forming a first self-organizing map using the first numeric representations, wherein the first numeric representations of the textual data are organized by similarities;

forming a second self-organizing map from second numeric representations generated from the organization of the first self-organizing map, wherein the second numeric representations are organized into clusters of similarities on the second self-organizing map; and

forming dialectic arguments from the second self-organizing map to interpret the textual data.

2. The computer implemented method of claim 1, wherein the textual data is reduced to a plurality of distinctive words.

3. The computer implemented method of claim 2, wherein the plurality of distinctive words are selected based on frequency of usage within the textual data.

4. The computer implemented method of claim 1, wherein the first numeric representations include a plurality of vectors.

5. The computer implemented method of claim 4, wherein the plurality of vectors include random numbers.

6. The computer implemented method of claim 4, wherein the plurality of vectors are trained onto the first self-organizing map.

7. The computer implemented method of claim 1, wherein a plurality of vectors are formed from the first self-organizing map.

8. The computer implemented method of claim 7, wherein the plurality of vectors from the first self-organizing map are used to train the second self-organizing map.

9. The computer implemented method of claim 8, wherein the plurality of vectors from the first self-organizing map are formed into the clusters on the second self-organizing map.

10. A method of interpreting textual data, comprising:

converting the textual data into first numeric representations;

forming a first self-organizing map using the first numeric representations;

forming a second self-organizing map from second numeric representations generated from the first self-organizing map, wherein the second numeric representations are organized into clusters on the second self-organizing map; and

forming dialectic arguments from the second self-organizing map to interpret the textual data.

11. The method of claim 10, wherein the textual data is reduced to a plurality of distinctive words.

12. The method of claim 11, wherein the plurality of distinctive words are selected based on frequency of usage within the textual data.

13. The method of claim 10, wherein the first numeric representations include a plurality of vectors.

14. The method of claim 13, wherein the plurality of vectors include random numbers.

15. The method of claim 13, wherein the plurality of vectors are trained onto the first self-organizing map.

16. The method of claim 10, wherein a plurality of vectors are formed from the first self-organizing map.

17. The method of claim 16, wherein the plurality of vectors from the first self-organizing map are used to train the second self-organizing map.

18. The method of claim 16, wherein the plurality of vectors from the first self-organizing map are formed into the clusters on the second self-organizing map.

19. A computer program product usable with a programmable computer processor having a computer readable program code embodied therein, comprising:

computer readable program code which converts the textual data into first numeric representations;

computer readable program code which forms a first self-organizing map using the first numeric representations;

computer readable program code which forms a second self-organizing map from second numeric representations generated from the first self-organizing map, wherein the second numeric representations are organized into clusters on the second self-organizing map; and

computer readable program code which forms dialectic arguments from the second self-organizing map to interpret the textual data.

20. The computer program product of claim 19, wherein the textual data is reduced to a plurality of distinctive words.

21. The computer program product of claim 20, wherein the plurality of distinctive words are selected based on frequency of usage within the textual data.

22. The computer program product of claim 19, wherein the first numeric representations include a plurality of vectors.

23. The computer program product of claim 22, wherein the plurality of vectors are trained onto the first self-organizing map.

24. The computer program product of claim 19, wherein a plurality of vectors are formed from the first self-organizing map.

25. The computer program product of claim 24, wherein the plurality of vectors from the first self-organizing map are used to train the second self-organizing map.

26. The computer program product of claim 24, wherein the plurality of vectors from the first self-organizing map are formed into the clusters on the second self-organizing map.

27. A computer system for interpreting textual data, comprising:

means for converting the textual data into first numeric representations;

means for forming a first self-organizing map using the first numeric representations;

means for forming a second self-organizing map from second numeric representations generated from the first self-organizing map, wherein the second numeric representations are organized into clusters on the second self-organizing map; and

means for forming dialectic arguments from the second self-organizing map to interpret the textual data.