SYSTEM AND METHOD FOR CONVERTING A NATURAL LANGUAGE QUERY INTO A LOGICAL QUERY

• US 20080104071A1
• Filed: 10/30/2007
• Published: 05/01/2008
• Est. Priority Date: 10/31/2006
• Status: Active Grant

• Current Assignee: Execue Incorporated
• Original Assignee: Execue Incorporated
• Inventors: Pragada, Sreenivasa Rao, Patil, Abhijit A., Dasari, Viswanath
• Granted Patent: US 8,924,197 B2
• Time in Patent Office: Days
• US Class Current: 707/6
• CPC Class Codes: G06F 16/24522 Translation of natural language queries to structured queries

Abstract

Disclosed are systems, methods, and computer readable media for converting a natural language query into a logical query. The method embodiment comprises receiving a natural language query and converting the natural language query using an extensible engine to generate a logical query, the extensible engine being linked to the toolkit and knowledge base. In one embodiment, a natural language query can be processed in a domain independent method to generate a logical query.

Claims

1. A method of converting a natural language query into a logical query, the method comprising:

receiving a natural language query;

converting the natural language query using an extensible engine to generate a logical query, the extensible engine being linked to the toolkit and knowledge base.

2. The method of claim 1, wherein the generated logical query is platform or data source independent.

3. The method of claim 1, wherein converting the natural language query includes parsing the natural language query repeatedly until every word is recognized as a domain, general, or an attribute concept, or until no additional information can be inferred about the natural language query within the scope of the toolkit and knowledge base.

4. The method of claim 1, wherein the knowledge base may include semantic frameworks explaining business or domain models, universal word patterns, mathematical or regular expressions, or conversion tables.

5. The method of claim 1, wherein the toolkit may include one or more of a speech tagger, spell checker, domain instance recognizer, word compactor, synonyms handlers, domain specific tagger, snowflake processor, ambiguity handlers, series recognizers, or word-pattern recognizers, each toolkit item being included in part or in whole.

6. The method of claim 1, wherein the extensible engine is not dependent on any one tool in the toolkit but instead uses a collection of tools, and wherein the extensible engine continues to operate when one or more tools are removed.

7. The method of claim 1, wherein the extensible engine converts the natural language query by a plurality of scans of the natural language query using the toolkit and the knowledge base, each scan having a different set of goals.

8. The method of claim 1, wherein the extensible engine is implemented as a collection of algorithms and/or data structures.

9. The method of claim 1, wherein the extensible engine has a main processing algorithm that engages portions of the toolkit and stores all intermediate results inside a plurality of data-structures.

10. The method of claim 9, wherein the main processing algorithm engages portions of the toolkit in no specific order.

11. The method of claim 9, wherein the plurality of data-structures is a multi-dimensional matrix-like data structure.

12. The method of claim 11, wherein the multi-dimensional matrix-like structure stores a plurality of labels describing query tokens or outcomes of each application of a tool to the natural language query.

13. The method of claim 9, wherein the extensible engine can recognize and resolve ambiguity when different tools in the toolkit yield dissimilar outcomes.

14. The method of claim 9, wherein the extensible engine assesses potential concepts and relationships between the concepts based on combining one or more outcomes.

15. The method of claim 9, wherein the extensible engine repeatedly applies tools, captures outcomes, and assesses the concepts and relationships based on the combination of outcomes until no additional information can be inferred within the scope of the toolkit and the knowledge base.

16. A system for converting a natural language query into a logical query, the system comprising:

a module configured to receive a natural language query;

a module configured to convert the natural language query using an extensible engine to generate a logical query, the extensible engine being linked to the toolkit and knowledge base.

17. The system of claim 16, wherein the generated logical query is platform or data source independent.

18. The system of claim 16, wherein converting the natural language query includes parsing the natural language query repeatedly until every word is recognized as a domain, general, or an attribute concept, or until no additional information can be inferred about the natural language query within the scope of the toolkit and knowledge base.

19. The system of claim 16, wherein the knowledge base may include semantic frameworks explaining business or domain models, universal word patterns, mathematical or regular expressions, or conversion tables.

20. The system of claim 16, wherein the toolkit may include one or more of a speech tagger, spell checker, domain instance recognizer, word compactor, synonyms handlers, domain specific tagger, snowflake processor, ambiguity handlers, series recognizers, or word-pattern recognizers, each toolkit item being included in part or in whole.

21. The system of claim 16, wherein the extensible engine is not dependent on any one tool in the toolkit but instead uses a collection of tools, and wherein the extensible engine can continue to operate when one or more tools are removed.

22. The system of claim 16, wherein the extensible engine processes the natural language query by a plurality of scans of the natural language query using the toolkit and the knowledge base, each scan having a different set of goals.

23. The system of claim 16, wherein the extensible engine is implemented as a collection of algorithms and/or data structures.

24. The system of claim 16, wherein the extensible engine has a main processing algorithm that engages portions of the toolkit and stores all intermediate results inside a plurality of data-structures.

25. The system of claim 24, wherein the main processing algorithm engages portions of the toolkit in no specific order.

26. The system of claim 24, wherein the extensible engine stores intermediate results methodically.

27. The system of claim 24, wherein the plurality of data-structures is a multi-dimensional matrix-like data structure.

28. The system of claim 27, wherein the multi-dimensional matrix-like structure stores a plurality of labels describing query tokens or outcomes of each application of a tool to the natural language query.

29. The system of claim 24, wherein the extensible engine can recognize and resolve ambiguity when different tools in the toolkit yield dissimilar outcomes.

30. The system of claim 24, wherein the extensible engine assesses potential concepts and relationships between the concepts based on combining one or more outcomes.

31. The system of claim 24, wherein the extensible engine repeatedly applies tools, captures outcomes, and assesses the concepts and relationships based on the combination of outcomes until no additional information can be inferred within the scope of the toolkit and the knowledge base.

32. A computer-readable medium storing instructions for converting a natural language query into a logical query, the instructions comprising:

receiving a natural language query;

converting the natural language query using an extensible engine to generate a logical query, the extensible engine being linked to the toolkit and knowledge base.

33. The computer-readable medium of claim 32, wherein the generated logical query is platform or data source independent.

34. The computer-readable medium of claim 32, wherein converting the natural language query includes parsing the natural language query repeatedly until every word is recognized as a domain, general, or an attribute concept, or until no additional information can be inferred about the natural language query within the scope of the toolkit and knowledge base.

35. The computer-readable medium of claim 32, wherein the knowledge base may include semantic frameworks explaining business or domain models, universal word patterns, mathematical or regular expressions, or conversion tables.

36. The computer-readable medium of claim 32, wherein the toolkit may include one or more of a speech tagger, spell checker, domain instance recognizer, word compactor, synonyms handlers, domain specific tagger, snowflake processor, ambiguity handlers, series recognizers, or word-pattern recognizers, each toolkit item being included in part or in whole.

37. The computer-readable medium of claim 32, wherein the extensible engine is not dependent on any one tool in the toolkit but instead uses a collection of tools, and wherein the extensible engine can continue to operate when one or more tools are removed.

38. The computer-readable medium of claim 32, wherein the extensible engine processes the natural language query by a plurality of scans of the natural language query using the toolkit and the knowledge base, each scan having a different set of goals.

39. The computer-readable medium of claim 32, wherein the extensible engine is implemented as a collection of algorithms and/or data structures.

40. The computer-readable medium of claim 32, wherein the extensible engine has a main processing algorithm that engages portions of the toolkit and stores all intermediate results inside a plurality of data-structures.

41. The method of claim 40, wherein the main processing algorithm engages portions of the toolkit in no specific order.

42. The method of claim 40, wherein the extensible engine stores intermediate results methodically.

43. The method of claim 40, wherein the plurality of data-structures is a multi-dimensional matrix-like data structure.

44. The method of claim 43, wherein the multi-dimensional matrix-like structure stores a plurality of labels describing query tokens or outcomes of each application of a tool to the natural language query.

45. The method of claim 40, wherein the extensible engine can recognize and resolve ambiguity when different tools in the toolkit yield dissimilar outcomes.

46. The method of claim 40, wherein the extensible engine assesses potential concepts and relationships between the concepts based on combining one or more outcomes.

47. The method of claim 40, wherein the extensible engine repeatedly applies tools, captures outcomes, and assesses the concepts and relationships based on the combination of outcomes until no additional information can be inferred within the scope of the toolkit and the knowledge base.