KNOWLEDGE REPRESENTATION SYSTEMS AND METHODS INCORPORATING INFERENCE RULES

• US 20120166373A1
• Filed: 01/06/2012
• Published: 06/28/2012
• Est. Priority Date: 03/30/2005
• Status: Active Grant

• Current Assignee: Primal Fusion Inc.
• Original Assignee: Primal Fusion Inc.
• Inventors: Sweeney, Peter Joseph, Ilyas, Ihab Francis, Oldford, Wayne, Zhou, Wu
• Granted Patent: US 10,002,325 B2
• Time in Patent Office: Days
• US Class Current: 706/14
• CPC Class Codes: G06F 16/84 Mapping; Conversion
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including both an elemental data structure and knowledge processing rules stored as machine-readable data and/or programming instructions. One or more of the knowledge processing rules may be applied to analyze an input complex KR to deconstruct its complex concepts and/or concept relationships to elemental concepts and/or concept relationships to be included in the elemental data structure. One or more of the knowledge processing rules may be applied to synthesize an output complex KR from the stored elemental data structure in accordance with context information. Methods of populating an elemental data structure and methods of synthesizing complex KRs from the elemental data structure may rely on linguistic inference rules and/or elemental inference rules.

Claims

1. A method of modifying a computer-readable elemental data structure of a knowledge representation system, the method comprising:

applying, using at least one processor executing stored program instructions, one or more first rules of analysis to deconstruct a knowledge representation into one or more elemental components;

adding, using at least one processor executing stored program instructions, data associated with the one or more elemental components to the elemental data structure;

inferring candidate data associated with the elemental data structure, the inferring comprising detecting in reference data, using at least one processor executing stored program instructions, language corresponding to a predetermined linguistic pattern;

modifying the elemental data structure to combine the candidate data and data associated with the one or more elemental components,

wherein the one or more elemental components are encoded as one or more computer-readable data structures storing data associated with the one or more elemental components, and

the reference communication is encoded as one or more computer-readable data structures storing data associated with the reference communication.

2. The method of claim 1, wherein the candidate data comprises a first elemental concept relationship indicating that a second elemental concept subsumes a first elemental concept.

3. The method claim 2, wherein detecting the language corresponding to the predetermined linguistic pattern comprises detecting in the reference data a first label associated with the first elemental concept, followed by a subsumptive expression, followed by a second label associated with the second elemental concept, the subsumptive expression implying that the second elemental concept subsumes the first elemental concept.

4. The method of claim 3, wherein the subsumptive expression comprises at least one of one or more predetermined words or one or more predetermined symbols.

5. The method of claim 3, wherein the subsumptive expression comprises at least one of “

is a”

, “

is an”

, “

is a field of”

, or “

is a type of”

.

6. The method of claim 2, wherein:

detecting the predetermined linguistic pattern in the reference data comprises detecting in the reference data a first label associated with the first elemental concept and a second label associated with the second elemental concept, a proximity of the first label to the second label being within a threshold proximity.

7. The method of claim 6, wherein the threshold proximity is at least one of a number of words, a number of sentences, or a number of a paragraphs.

8. The method of claim 1, wherein the candidate data comprises a first elemental concept relationship indicating that a second elemental concept defines a first elemental concept.

9. The method claim 8, wherein:

detecting that the predetermined linguistic pattern in the reference data comprises detecting in the reference data a first label associated with the first elemental concept, followed by a definitional expression, followed by a second label associated with the second elemental concept, the definitional expression implying that the first elemental concept is defined by the second elemental concept.

10. The method of claim 1, further comprising inferring second candidate data associated with the elemental data structure, the inferring the second candidate data comprising:

identifying a first elemental concept in the elemental data structure, the first elemental concept being defined by one or more first characteristic concepts;

identifying a second elemental concept in the elemental data structure, the second elemental concept being defined by one or more second characteristic concepts; and

determining that each characteristic concept in the one or more second characteristic concepts is in the one or more first characteristic concepts or subsumes a characteristic concept in the one or more first characteristic concepts.

11. The method of claim 2, wherein:

the elemental data structure comprises the first elemental concept and the second elemental concept; and

modifying the elemental data structure to combine the candidate data and data associated with the one or more elemental components comprises adding the first elemental concept relationship to the elemental data structure.

12. The method of claim 1, wherein the candidate data indicates that a second elemental concept does not subsume a first elemental concept.

13. The method of claim 12, wherein:

the elemental data structure comprises an elemental concept relationship between the first elemental concept and the second elemental concept, the elemental concept relationship indicating that the second elemental concept subsumes the first elemental concept; and

modifying the elemental data structure to combine the candidate data and data associated with the one or more elemental components comprises one of removing the elemental concept relationship from the elemental data structure or reducing a probability associated with the elemental concept relationship.

14. A method of modifying a computer-readable elemental data structure of a knowledge representation system, the method comprising:

applying, using at least one processor executing stored program instructions, one or more first rules of analysis to deconstruct a knowledge representation into one or more elemental components;

adding, using at least one processor executing stored program instructions, data associated with the one or more elemental components to the elemental data structure;

inferring a candidate probability of an elemental concept relationship, the inferring comprising applying, using at least one processor executing stored program instructions, one or more elemental inference rules to the elemental data structure; and

modifying the elemental data structure to combine the candidate probability and data associated with the one or more elemental components,

wherein the one or more elemental components are encoded as one or more computer-readable data structures storing data associated with the one or more elemental components.

15. The method of claim 14, wherein the elemental concept relationship indicates that a second elemental concept subsumes a first elemental concept.

16. The method of claim 15, wherein applying the one or more elemental inference rules to the elemental data structure comprises:

identifying the first elemental concept in the elemental data structure, the first elemental concept being defined by one or more first characteristic concepts, the one or more first characteristic concepts comprising a first characteristic concept;

identifying the second elemental concept in the elemental data structure, the second elemental concept being defined by one or more second characteristic concepts, the one or more second characteristic concepts comprising a second characteristic concept; and

calculating, as the candidate probability, a probability that each characteristic concept in the one or more second characteristic concepts is in the one or more first characteristic concepts or subsumes a characteristic concept in the one or more first characteristic concepts.

17. The method of claim 14, wherein the elemental concept relationship indicates that a second elemental concept defines a first elemental concept.

18. The method of claim 15, wherein the one or more elemental inference rules are applied to the elemental data structure in response to receiving context information from a user, the context information being associated with at least one of the first elemental concept or the second elemental concept.

19. The method of claim 15, wherein:

the one or more elemental inference rules are applied to the elemental data structure in response to obtaining data indicating that a label associated with at least one of the first elemental concept or the second elemental concept appears in reference data at a rate that exceeds a threshold rate.

20. The method of claim 14, wherein:

the elemental data structure comprises the elemental concept relationship and a probability associated with the elemental concept relationship; and

modifying the elemental data structure to combine the candidate data and data associated with the one or more elemental components comprises replacing the probability associated with the elemental concept relationship with one of the probability data, an average of the probability data and the probability associated with the elemental concept relationship, or a function of the probability data and the probability associated with the elemental concept relationship.

21. A knowledge representation apparatus for modifying a computer-readable elemental data structure, the apparatus comprising:

an inference unit configured to;

infer candidate data associated with the elemental data structure by applying, using at least one processor executing stored program instructions, one or more inferential analysis rules to at least one of reference data or the elemental data structure,

modify the elemental data structure by incorporating the candidate data into the elemental data structure.

22. The apparatus of claim 21, further comprising:

an analysis unit configured to;

apply, using at least one processor executing stored program instructions, one or more first rules of analysis to deconstruct a knowledge representation into one or more elemental components, and

add data associated with the one or more elemental components to the elemental data structure.