System and method of self-learning conceptual mapping to organize and interpret data
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.
1. A computer implemented method of researching textual data sources, comprising:
retrieving textual data from a database, the textual data including emails or police reports;
converting the 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;
displaying the second self-organizing map on a display screen;
forming dialectic arguments from the second self-organizing map to interpret the textual data; and
storing the first self-organizing map, second self-organizing map, or dialectic arguments in a computer-readable medium.
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;
displaying the second self-organizing map on a display screen; 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;
computer readable program code which displays the second self-organizing map on a display screen; 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;
means for displaying the second self-organizing map on a display screen; and
means for forming dialectic arguments from the second self-organizing map to interpret the textual data.