Method using ontology and user query processing to solve inventor problems and user problems

• US 20060047632A1
• Filed: 08/12/2004
• Published: 03/02/2006
• Est. Priority Date: 08/12/2004
• Status: Active Grant

• Current Assignee: Beijing Iwint Technology Limited
• Original Assignee: Iwint International Holdings Inc.
• Inventors: Zhang, Guoming
• Granted Patent: US 7,685,118 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/367 Ontology

Abstract

A system, method and computer program product for solving a problem based on ontology methods for data/knowledge presentation and processing, implemented in a linguistic processing module. The basic components of the linguistic processing module may be a linguistic knowledge base (KB), an ontology KB, and/or an expert KB. The problem solved may include a user or an inventor problem. The method may include storing a user query which may include a structured, or a non-structured description, parsing a non-structured query to create a structured query including a formal semantic representation of the query, semantically expanding the formal semantic representation to provide searching for relevant solutions, searching the solutions in the expert KB, and semantically sorting the solutions to produce a list of solutions of the problem.

Claims

1. A method for solving a problem comprising:

storing a user query which comprises at least one of a structured or a non-structured description of the problem;

parsing of said user query wherein said user query is non-structured, to create a structured user query, wherein said structured user query comprises a formal semantic representation of said user query;

semantically expanding said formal semantic representation of said user query to provide searching for at least one solution relevant to the problem;

searching said at least one solution in an Expert Knowledge Base; and

semantically sorting said at least one solution to produce a list of at least one solution for the problem.

2. The method of claim 1, wherein said user query comprises a structured query, is presented in semantic verb parameter object (VPO) format.

3. The method of claim 1, wherein said problem comprises at least one of an inventor problem, or a user problem.

4. The method of claim 1, wherein said non-structured user query is presented in a natural language format.

5. The method according to claim 1, wherein said semantically expanding comprises searching for all relevant of said at least one solution relevant to the problem.

6. The method of claim 1, wherein said parsing step comprises:

lexically and grammatically analyzing said non-structured user query; and

semantically analyzing said non-structured query to create said formal semantic representation of said user query.

7. The method of claim 6, wherein said lexically and grammatically analyzing is based on at least one of a Linguistic Knowledge Base, or an Ontology Knowledge Base to recognize noun and verb phrases of said user query.

8. The method of claim 6, wherein said semantically analyzing is based on lexically and grammatically analyzing said user query and lemmatization dictionary and linguistic algorithms for noun and verb phrases canonization and list of parameters from a Linguistic Knowledge Base to produce said formal semantic representation of said user query in a verb parameter object (VPO) format.

9. The method of claim 1, wherein said semantically expanding is based on expanding based on an Ontology Knowledge Base producing a semantic expansion of said formal semantic representation of said user query.

10. The method of claim 9, wherein said Ontology Knowledge Base comprises a database of at least one of a term, or a relation between two or more of said at least one terms for different knowledge domains.

11. The method of claim 10, wherein said term comprises at least one term-concept, or term-verb.

12. The method of claim 10, wherein said relation for a term-verb comprises at least one of a direct synonym, or a syntactic synonym.

13. The method of claim 10, wherein said relation for term-concepts comprises at least one of adirect synonym, a kind-of, or an association.

14. The method of claim 9, wherein said Ontology Knowledge Base comprises at least one of technology, or tools for domain knowledge experts to at least one of edit, or enrich said Ontology Knowledge Base.

15. The method of claim 9, wherein said semantic expansion comprises a list of search patterns relevant to said formal semantic representation of said user query.

16. The method of claim 15, wherein every search pattern from said list of patterns includes its relevance value of said every search pattern compared to said formal semantic representation of user query.

17. The method of claim 1, wherein said searching comprises searching based on a list of at least one search pattern and said Expert Knowledge Base to locate all available of said solutions of the problem.

18. The method of claim 17, wherein said Expert Knowledge Base is a knowledge base of technical solutions extracted from a natural language document.

19. The method of claim 18, wherein said natural language document comprises at least one of a patent or an article.

20. The method of claim 18, wherein said technical solutions are presented in natural language sentences and in subject verb parameter object (SVPO) format.

21. The method of claim 17, wherein said Expert Knowledge Base is provided with technology and tools for domain knowledge experts to edit and enrich said Expert Knowledge Base.

22. The method of claim 17, wherein said locating of all available of said solutions is performed only for those technical solutions from said Expert Knowledge Base, where V, P and O fields match the appropriate fields of the search patterns.

23. The method of claim 22, wherein said technical solutions located are given similar relevance values to relevance values of the search patterns.

24. The method of claim 1, wherein said semantically sorting comprises producing a list of solutions based on types of semantic expansion and relevance values of technical solutions of the problem.

25. The method of claim 24, wherein said list of solutions is first sorted on the basis of type of semantic expansion in the following order:

precise solutions, particular solutions, general solutions and analogous solutions.

26. The method of claim 25, wherein said list of solutions inside of every sublist of solutions obtained in accordance with said type of semantic expansion, is further sorted according to the relevance values of said technical solutions.

27. A system for solving a problem comprising:

storing means for storing a user query which comprises at least one of a structured or a non-structured description of the problem;

parsing means for parsing of said non-structured description of said user query to create said structured description of said user query, wherein said user query comprises a formal semantic representation of said user query;

semantically expanding means for semantically expanding said formal semantic representation of said user query to provide searching for at least one solution relevant to the problem;

searching means for searching said at least one solution in an Expert Knowledge Base; and

semantically sorting means for semantically sorting said at least one solution to produce a list of solutions for the problem.

28. The system of claim 27, wherein the problem comprises at least one of an inventor problem, or a user problem.

29. The system of claim 27, wherein said semantically expanding means comprises searching for all relevant of said at least one solution relevant to the problem.

30. The system of claim 27, wherein said parsing means comprises:

means for lexically and grammatically analyzing said non-structured user query; and

means for semantically analyzing said non-structured query to create said formal semantic representation of said user query.

31. The system of claim 27, wherein said semantically expanding means is based on an Ontology Knowledge Base to produce a semantic expansion of said formal semantic representation of said user query.

32. The system of claim 31, wherein said Ontology Knowledge Base comprises a database of at least one of at least one term, or a relation between two or more of said at least one terms for different knowledge domains.

33. The system of claim 27, wherein said searching means comprises searching based on a list of at least one search pattern and said Expert Knowledge Base to locate all available of said solutions of the problem.

34. The system of claim 33, wherein said Expert Knowledge Base comprises a knowledge base of technical solutions extracted from a natural language document.

35. The system of claim 27, wherein said semantically sorting means comprises means for producing a list of solutions based on type of semantic expansion and relevance values of technical solutions of the problem.

36. The system of claim 35, wherein said list of solutions comprises means for first sorting on the basis of type of semantic expansion in the following order:

precise solutions, particular solutions, general solutions and analogous solutions.

37. The system of claim 36, wherein said list of solutions inside of every sublist of solutions obtained in accordance with type of semantic expansion, is further sorted according to the relevance values of said technical solutions.

38. A machine readable media that provides instructions, which when executed by a computing platform, cause said computing platform to perform operations comprising a method of solving problems comprising:

storing a user query comprising at least one of a structured or non-structured description of the problem in verb parameter object (VPO) format;

parsing said non-structured description of said user query to create said structured description of said user query wherein said structured description of said user query comprises a formal semantic representation of said user query;

semantically expanding said formal semantic representation of said user query to provide searching for at least one solution relevant to the problem;

searching said at least one solution in an Expert Knowledge Base; and

semantically sorting said at least one solution to produce a list of solutions for the problem.