SYSTEMS AND METHODS FOR APPLYING STATISTICAL INFERENCE TECHNIQUES TO KNOWLEDGE REPRESENTATIONS

• US 20120166372A1
• 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: Ilyas, Ihab Francis, Zhou, Wu, Oldford, Wayne
• Granted Patent: US 8,849,860 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 a complex KR from the elemental data structure may rely on statistical inference techniques.

Claims

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

estimating, using at least one processor executing stored program instructions, a relevance associated with an elemental component of the elemental data structure, estimating the relevance comprising estimating a frequency of occurrence in reference data of one or more labels associated with the elemental component; and

modifying the elemental data structure, comprising storing the relevance in data associated with the elemental component;

wherein the elemental component is encoded as a computer-readable data structure storing the data associated with the elemental component.

2. The method of claim 1, further comprising applying, using at least one processor executing stored program instructions, one or more rules to deconstruct a knowledge representation into one or more elemental components comprising the elemental component, wherein modifying the elemental data structure further comprises adding data associated with the one or more elemental components to the elemental data structure.

3. The method of claim 1, wherein estimating the frequency of occurrence of the one or more labels comprises obtaining statistics associated with reference data.

4. The method of claim 1, wherein:

the relevance associated with the elemental component of the elemental data structure comprises a concept relevance associated with an elemental concept of the elemental data structure, and

the one or more labels associated with the elemental component comprises a label associated with the elemental concept.

5. The method of claim 1, wherein the relevance associated with the elemental component of the elemental data structure comprises a relationship relevance associated with an elemental concept relationship of the elemental data structure.

6. The method of claim 4, wherein estimating the frequency of occurrence in the reference data of the one or more labels associated with the elemental component comprises estimating a term frequency of the label within at least a portion of the reference data.

7. The method of claim 4, wherein estimating the frequency of occurrence in the reference data of the one or more labels associated with the elemental component comprises estimating a term-document frequency of the label within at least a portion of the reference data.

8. The method of claim 4, wherein estimating the frequency of occurrence in the reference data of the one or more labels associated with the elemental component comprises estimating an inverse document frequency of the label within at least a portion of the reference data.

9. The method of claim 5, wherein:

estimating the frequency of occurrence in the reference data of the one or more labels associated with the elemental component comprises estimating a term-document frequency according to a formula Docs(L1 and L2)/NumDocs,

the elemental concept relationship relates a first elemental concept and a second elemental concept,

L1 represents a first label associated with the first elemental concept,

L2 represents a second label associated with the second elemental concept;

Docs(L1 and L2) represents a number of documents in at least a portion of the reference data that contain the first label and the second label, and

NumDocs represents a number of documents in the at least a portion of the reference data.

10. The method of claim 9, wherein a document comprises at least one of a sentence, a plurality of sentences, a paragraph, or a plurality of paragraphs.

11. The method of claim 9, wherein NumDocs represents the number of documents in the at least a portion of the reference data that contain at least one of the first label or the second label.

12. The method of claim 1, wherein estimating the frequency of occurrence in the reference data of the one or more labels associated with the elemental component comprises using a search engine to search the reference data for documents containing the one or more labels associated with the elemental component.

13. A method of modifying a computer-readable graphical model associated with an elemental data structure, the elemental data structure comprising one or more elemental components, the one or more elemental components comprising one or more elemental concepts and one or more elemental concept relationships, the method comprising:

obtaining the graphical model associated with the elemental data structure, the graphical model comprising one or more graphical components, the one or more graphical components comprising;

one or more nodes corresponding to the respective one or more elemental concepts of the elemental data structure, and

one or more edges incident on nodes of the one or more nodes, the one or more edges corresponding to the respective one or more elemental concept relationships of the elemental data structure;

estimating, using at least one processor executing stored program instructions, a semantic coherence of an elemental component; and

modifying the graphical model by assigning a probability corresponding to the semantic coherence to a graphical component of the graphical model,

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

the one or more graphical components are encoded as one or more respective computer-readable data structures storing data associated with the one or more graphical components,

the elemental component is encoded as a computer readable data structure storing data associated with the elemental component, and

the graphical component is encoded as a computer readable data structure storing data associated with the graphical component.

14. The method of claim 13, wherein estimating the semantic coherence comprises estimating a frequency of occurrence in reference data of one or more labels associated with the elemental component.

15. The method of claim 13, wherein:

the elemental component comprises an elemental concept relationship, the elemental concept relationship relating a first elemental concept and a second elemental concept; and

estimating the semantic coherence of the elemental component comprises calculating a joint probability associated with the first elemental concept and the second elemental concept.

16. The method of claim 13, further comprising modifying the elemental data structure, wherein:

the elemental component comprises an elemental concept relationship, the elemental concept relationship being between a first elemental concept and a second elemental concept of the one or more elemental concepts,

the one or more labels comprise a first label associated with the first elemental concept and a second label associated with the second elemental concept,

the graphical component comprises an edge of the graphical model, the edge corresponding to the elemental concept relationship, and

modifying the elemental data structure comprises adding the elemental concept relationship to the elemental data structure in a case where the semantic coherence exceeds a threshold.

17. The method of claim 13, further comprising modifying the elemental data structure, wherein:

the elemental component comprises an elemental concept relationship, the elemental concept relationship being between a first elemental concept and a second elemental concept of the one or more elemental concepts,

the one or more labels comprise a first label associated with the first elemental concept and a second label associated with the second elemental concept,

the graphical component comprises an edge of the graphical model, the edge corresponding to the elemental concept relationship,

the elemental data structure comprises the elemental concept relationship, and

modifying the elemental data structure comprises removing the elemental concept relationship from the elemental data structure in a case where the semantic coherence of the elemental concept relationship is less than a threshold.

18. The method of claim 13, wherein:

the elemental component comprises an elemental concept,

the one or more labels comprise a first label associated with the elemental concept,

the graphical component comprises a node of the graphical model, the node corresponding to the elemental concept, and

modifying the elemental data structure comprises adding the elemental concept to the elemental data structure in a case where the semantic coherence exceeds a threshold.

19. The method of claim 13, wherein:

the elemental component comprises an elemental concept,

the one or more labels comprise a first label associated with the elemental concept,

the graphical component comprises a node of the graphical model, the node corresponding to the elemental concept,

the elemental data structure comprises the elemental concept, and

modifying the elemental data structure comprises removing the elemental concept from the elemental data structure in a case where the semantic coherence of the elemental concept is less than a threshold.

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

a probabilistic unit configured to;

estimate a probability associated with an elemental component of the elemental data structure, estimating the probability comprising estimating a frequency of occurrence in reference data of one or more labels associated with the elemental component; and

modify the elemental data structure, comprising storing the probability in data associated with the elemental component;

wherein the elemental component is encoded as a computer-readable data structure storing the data associated with the elemental component.