Systems and methods for analyzing and synthesizing complex knowledge representations

• US 9,104,779 B2
• Filed: 06/21/2011
• Issued: 08/11/2015
• Est. Priority Date: 03/30/2005
• Status: Active Grant

• Current Assignee: Primal Fusion Inc.
• Original Assignee: Primal Fusion Inc.
• Inventors: Hunt, Anne Jude, Black, Alexander David, Sweeney, Peter Joseph, Ilyas, Ihab Francis
• Primary Examiner(s): Fleurantin, Jean B
• Application Number:
  US13/165,423
• Publication Number:
  US 20110320396A1
• Time in Patent Office: 1,512 Days
• Field of Search: 707/791, 707/794, 707/796, 707/827, 706/46, 706/47
• US Class Current: 1/1
• CPC Class Codes: G06F 16/84 Mapping; Conversion
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

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 an input context. Multiple input complex KRs of various types may be analyzed and deconstructed to populate the elemental data structure, and input complex KRs may be transformed through the elemental data structure to output complex KRs of different types, providing semantic interoperability to KRs of different types and/or KR models.

Claims

1. A computer-implemented method for generating a complex knowledge representation, the method comprising:

receiving input indicating a request context; and

generating, with a processor, in accordance with the request context, a complex knowledge representation of a first type from an elemental data structure representing at least one elemental concept, at least one elemental concept relationship, or at least one elemental concept and at least one elemental concept relationship, the generating comprising;

applying a first set of one or more rules to the elemental data structure, wherein the first set of one or more rules differs by at least one rule from a second set of one or more rules applicable to the elemental data structure to generate a complex knowledge representation of a second type different from the first type;

based on the application of the first set of one or more rules, synthesizing, in accordance with the request context, one or more additional concepts, one or more additional concept relationships, or one or more additional concepts and one or more additional concept relationships; and

using at least one of the additional concepts, at least one of the additional concept relationships, or at least one of the additional concepts and at least one of the additional concept relationships, generating the complex knowledge representation of the first type in accordance with the request context.

2. The method according to claim 1, wherein synthesizing includes applying at least one technique selected from the group consisting of concept analysis, faceted classification synthesis, semantic synthesis, and dynamic taxonomies.

3. The method according to claim 1, wherein receiving input includes receiving at least one item selected from the group consisting of a text query, a search term, a seed concept, and a complex KR request.

4. The method according to claim 1, wherein generating the complex knowledge representation includes at least one act selected from the group consisting of 1) generating less than a predetermined number of concepts, and 2) generating only concepts within a predetermined hierarchical distance of a seed concept.

5. The method according to claim 1, further comprising outputting the generated complex knowledge representation to a user or operator.

6. The method according to claim 1, further comprising generating the elemental data structure at least in part by deconstructing an original knowledge representation to derive the at least one elemental concept from at least one more complex concept of the original knowledge representation.

7. A computer-implemented method for deconstructing an original knowledge representation, the method comprising:

receiving input corresponding to the original knowledge representation;

applying, with a processor, a set of one or more rules to deconstruct the original knowledge representation into one or more elemental concepts, one or more elemental concept relationships, or one or more elemental concepts and one or more elemental concept relationships;

including representation of at least one of the elemental concepts, at least one of the elemental concept relationships, or at least one of the elemental concepts and at least one of the elemental concept relationships in an elemental data structure; and

generating a complex knowledge representation of a first type by applying a first set of one or more rules to the elemental data structure to transform at least a portion of the elemental data structure into the complex knowledge representation of the first type, wherein the first set of one or more rules differs by at least one rule from a second set of one or more rules applicable to the elemental data structure to generate a complex knowledge representation of a second type different from the first type.

8. The method of claim 7, further comprising synthesizing a new complex knowledge representation from the elemental data structure by applying a third set of rules to the elemental data structure.

9. The method according to claim 7, wherein generating the complex knowledge representation of the first type comprises:

synthesizing, in accordance with a request context, one or more additional concepts, one or more additional concept relationships, or one or more additional concepts and one or more additional concept relationships; and

using at least one of the additional concepts, at least one of the additional concept relationships, or at least one of the additional concepts and at least one of the additional concept relationships to generate the complex knowledge representation of the first type in accordance with the request context.

10. The method according to claim 7, further comprising storing the first set of one or more rules together with the second set of one or more rules.

11. The method according to claim 7, further comprising selecting the set of one or more rules based on the original knowledge representation.

12. The method according to claim 7, further comprising measuring semantic coherence of the elemental data structure.

13. The method according to claim 12, wherein measuring the semantic coherence comprises determining a term-document frequency.

14. The method according to claim 7, further comprising applying at least one technique selected from the group consisting of text analyses, statistical clustering, linguistic analyses, facet analyses, natural language processing and use of semantic knowledge-bases, to deconstruct the original knowledge representation.

15. The method according to claim 7, further comprising disambiguating the elemental concepts.

16. The method according to claim 7, wherein the elemental concepts include morphemes.

17. The method according to claim 7, wherein applying the set of one or more rules comprises deconstructing the original knowledge representation to a predetermined level of elementality.

18. A computer-implemented method for supporting semantic interoperability between knowledge representations, the method comprising:

for each input knowledge representation of a plurality of input knowledge representations, applying, with a processor, a set of one or more rules to deconstruct the input knowledge representation into one or more elemental concepts, one or more elemental concept relationships, or one or more elemental concepts and one or more elemental concept relationships;

with a processor, including representation of at least one of the elemental concepts, at least one of the elemental concept relationships, or at least one of the elemental concepts and at least one of the elemental concept relationships for each of the plurality of input knowledge representations in a shared elemental data structure; and

generating a complex knowledge representation of a first type by applying a first set of one or more rules to the shared elemental data structure to transform at least a portion of the shared elemental data structure into the complex knowledge representation of the first type, wherein the first set of one or more rules differs by at least one rule from a second set of one or more rules applicable to the elemental data structure to generate a complex knowledge representation of a second type different from the first type.