Method and system for analysis of intended meaning of natural language

1. A method comprising:

determining one or more semantic efficiency values of a semantic network, wherein the semantic network includes a plurality of context nodes defining a stored natural language context, and wherein the semantic network is represented by data stored in a memory of a computer system;

rearranging a topology of the semantic network to improve at least one of the one or more semantic efficiency values;

receiving a natural language input at the computer system, wherein the natural language input includes one or more words;

analyzing at least one of the one or more words of the natural language input using the rearranged semantic network;

determining a contextual meaning for the natural language input using one or more of the analyzed words, wherein the contextual meaning for the natural language input is based on the plurality of context nodes in the rearranged semantic network; and

generating an output based on the determined contextual meaning.

2. The method of claim 1 , wherein the analyzing includes:

analyzing at least one of the one or more words of the natural language input using a plurality of candidate nodes and at least one contextual distance; and

determining at least one preferred contextual distance.

3. The method of claim 2 , wherein determining the contextual meaning for the natural language input includes:

applying the at least one contextual distance to the plurality of candidate nodes to produce the contextual meaning for the natural language input.

4. The method of claim 3 , wherein analyzing at least one of the one or more words of the natural language input using the plurality of candidate nodes and at least one contextual distance includes:

identifying a plurality of links, wherein one or more of the plurality of links traverse from one of the plurality of candidate nodes to one of the plurality of context nodes; and

computing each of the at least one contextual distance for each of the plurality of candidate nodes, each of the at least one contextual distance occurring between one of the plurality of candidate nodes and one of the plurality of context nodes.

5. The method of claim 4 , wherein determining at least one preferred contextual distance includes:

comparing each of the at least one contextual distance to one another.

6. The method of claim 5 , wherein determining at least one preferred contextual distance further includes:

selecting a subset of candidate nodes from the plurality of candidate nodes.

7. The method of claim 6 , wherein determining at least one preferred contextual distance further includes:

determining a preferred contextual meaning corresponding to the natural language input;

wherein each of the subset of candidate nodes has an associated contextual distance approximating an optimal contextual distance, such that each of the input nodes has at least one candidate node selected from the subset of candidate nodes, the at least one associated candidate node being the preferred contextual meaning for the corresponding one from the set of input nodes.

8. The method of claim 7 , wherein each of the subset of candidate nodes inherits from a common semantic network node within the semantic network.

9. The method of claim 7 , wherein determining at least one preferred contextual distance further includes:

determining a greatest minimal distance among each of the plurality of candidate nodes and each of the plurality of context nodes.

10. The method of claim 7 , wherein, if no links are identified as traversing among one of the plurality of candidate nodes and any one of the plurality of context nodes, estimating a distance between each of the plurality of candidate nodes and each of the plurality of context nodes.

11. The method of claim 7 , wherein each of the plurality of links has a corresponding one of a plurality of semantic link types.

12. The method of claim 11 , wherein no links are identified as traversing from one of the plurality of candidate nodes to one from the plurality of context nodes for at least one of the plurality of semantic link types.

13. The method of claim 12 , wherein identifying the plurality of links includes:

excluding each of the plurality of links having the at least one of the plurality of semantic link types for which no links are identified.

14. The method of claim 1 , wherein the computer system comprises a voice recognition capability.

15. The method of claim 14 , wherein a plurality of input candidate nodes are received via the voice recognition capability of the computer system.

16. The method of claim 1 , wherein the computer system is a household appliance.

17. The method of claim 1 , wherein the computer system is a hand-held device.

18. The method of claim 17 , wherein the hand-held device allows performance of network functions.

19. The method of claim 1 , wherein the computer system comprises a voice synthesis capability.

20. The method of claim 1 , wherein generating the output based on the determined contextual meaning includes:

providing a voice synthesis output of the determined contextual meaning corresponding to the natural language input.

21. The method of claim 1 , wherein the natural language input comprises a search request, and wherein generating the output based on the determined contextual meaning includes:

generating a search result corresponding to the determined contextual meaning for the natural language input comprising the search request.

22. The method of claim 1 , wherein the natural language input comprises a customer support query, and wherein generating the output based on the determined contextual meaning includes:

providing a customer support function corresponding to the determined contextual meaning for the customer support query.

23. The method of claim 1 , wherein at least one of the one or more semantic efficiency values relates to a balance of a subtree of the semantic network.

24. A system comprising:

a processor configured to execute instructions; and

a memory coupled to the processor, wherein the memory stores data representing a semantic network, wherein the semantic network includes a plurality of context nodes defining a stored natural language context, and wherein the memory stores program instructions executable by the processor to:

determine one or more semantic efficiency values of the semantic network;

rearrange a topology of the semantic network to improve at least one of the one or more semantic efficiency values;

receive a natural language input at the system, wherein the natural language input includes one or more words;

analyze at least one of the one or more words of the natural language input using the rearranged semantic network;

determine a contextual meaning for the natural language input using one or more of the analyzed words of the natural language input, wherein the contextual meaning or the natural language input is based on the plurality of context nodes in the rearranged semantic network; and

generate an output based on the determined contextual meaning.

25. The system of claim 24 , wherein the natural language input comprises a search request, and wherein, in generating the output based on the determined contextual meaning, the program instructions are executable by the processor to:

generate a search result corresponding to the determined contextual meaning for the natural language input comprising the search request.

26. The system of claim 24 , wherein the natural language input comprises a customer support query, and wherein, in generating the output based on the determined contextual meaning, the program instructions are executable by the processor to:

provide a customer support function corresponding to the determined contextual meaning for the customer support query.

27. The system of claim 24 , wherein at least one of the one or more semantic efficiency values relates to a balance of a subtree of the semantic network.

28. A computer usable storage medium comprising program instructions, wherein the program instructions are executable to implement:

determining one or more semantic efficiency values of a semantic network, wherein the semantic network includes a plurality of context nodes defining a stored natural language context, and wherein the semantic network is represented by data stored in a memory of a computer system;

rearranging a topology of the semantic network to improve at least one of the one or more semantic efficiency values;

receiving a natural language input at the computer system, wherein the natural language input includes one or more words;

analyzing at least one of the one or more words of the natural language input using the rearranged semantic network;

determining a contextual meaning for the natural language input using one or more of the analyzed words of the natural language input, wherein the contextual meaning for the natural language input is based on the plurality of context nodes in the rearranged semantic network; and

generating an output based on the determined contextual meaning.

29. The computer usable storage medium of claim 28 , wherein the natural language input comprises a search request, and wherein generating the output based on the determined contextual meaning includes:

generating a search result corresponding to the determined contextual meaning for the natural language input comprising the search request.

30. The computer usable storage medium of claim 28 , wherein the natural language input comprises a customer support query, and wherein generating the output based on the determined contextual meaning includes:

providing a customer support function corresponding to the determined contextual meaning for the customer support query.

31. The computer usable storage medium of claim 28 , wherein at least one of the one or more semantic efficiency values relates to a balance of a subtree of the semantic network.