System and Method of Self-Learning Conceptual Mapping to Organize and Interpret Data

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.

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.