Processes, data structures, and apparatuses for representing knowledge

• US 20070083492A1
• Filed: 09/27/2005
• Published: 04/12/2007
• Est. Priority Date: 09/27/2005
• Status: Active Grant

• Current Assignee: Battelle Memorial Institute
• Original Assignee: Battelle Memorial Institute
• Inventors: Harvey, William, Tratz, Stephen, Paulson, Patrick, Whiting, Mark, Chappell, Alan, Thomson, Judith, Butner, R., Hohimer, Ryan
• Granted Patent: US 8,023,739 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

Processes, data structures, and apparatuses to represent knowledge are disclosed. The processes can comprise labeling elements in a knowledge signature according to concepts in an ontology and populating the elements with confidence values. The data structures can comprise knowledge signatures stored on computer-readable media. The knowledge signatures comprise a matrix structure having elements labeled according to concepts in an ontology, wherein the value of the element represents a confidence that the concept is present in an information space. The apparatus can comprise a knowledge representation unit having at least one ontology stored on a computer-readable medium, at least one data-receiving device, and a processor configured to generate knowledge signatures by comparing datasets obtained by the data-receiving devices to the ontologies.

Claims

1. A process for representing knowledge comprising:

labeling elements in a knowledge signature according to concepts in an ontology and populating the elements with confidence values, wherein the confidence values represent a confidence that the concepts are present in an information space.

2. The process as recited in claim 1, wherein the knowledge signature is structured as a matrix of the elements.

3. The process as recited in claim 2, wherein the knowledge signature is a real-valued vector.

4. The process as recited in claim 1, wherein said populating comprises generating an observed confidence value from a dataset according to an observation engine.

5. The process as recited in claim 4, wherein the data set comprises information selected from the group consisting of text documents, video segments, audio segments, images, graphs, database records, sensor data, and combinations thereof.

6. The process as recited in claim 4, wherein the observation engine comprises a program module selected from the group consisting of image recognition software, text symbol recognition software, audio recognition software, and combinations thereof.

7. The process as recited in claim 4, wherein said populating further comprises generating inferred confidence values from at least one observed confidence value according to at least one refinement engine.

8. The process as recited in claim 7, wherein the refinement engine comprises a program module on a computer-readable medium, said program module comprising definitions of concept relationships.

9. The process as recited in claim 8, wherein the concept relationships are selected from the group consisting of subsumption, aggregation, and combinations thereof.

10. The process as recited in claim 1, further comprising generating a concept signature for each concept in the ontology, generating at least one dataset signature from a dataset, and performing a similarity measure between the concept signatures and the dataset signatures to determine confidence values.

11. The process as recited in claim 10, wherein the similarity measure comprises calculating Euclidean distances, cosine coefficients, or combinations thereof.

12. The process as recited in claim 1, further comprising comparing a plurality of knowledge signatures, wherein each knowledge signature is generated from a different data set.

13. The process as recited in claim 12, wherein the data sets comprise disparate data types.

14. The process as recited in claim 12, wherein said comparing comprises selecting a mode group, transforming the knowledge signatures into reduced-dimensional representations, and performing a similarity measure between the transformed knowledge signatures.

15. The process as recited in claim 14, wherein the similarity measure comprises calculating Euclidian distances, cosine coefficients, or combinations thereof.

16. The process as recited in claim 14, further comprising indexing the reduced-dimensional representation in existing data structures.

17. A computer-readable medium having stored thereon a knowledge signature, the knowledge signature comprising a matrix structure having elements labeled according to concepts in an ontology, wherein the value of the element represents a confidence that the concept is present in an information space.

18. The computer-readable medium as recited in claim 17, wherein the knowledge signature comprises a real-valued vector.

19. The computer-readable medium as recited in claim 17, wherein related concepts are grouped into mode groups.

20. An apparatus comprising a knowledge representation unit comprising at least one ontology stored on a computer-readable medium, at least one data-receiving device, and a processor;

wherein the processor is communicably connected to the computer readable medium and the data-receiving devices and generates knowledge signatures by comparing datasets obtained by the data-receiving devices to the ontologies.

21. The apparatus as recited in claim 20, further comprising at least one observation engine, wherein the observation engine populates the knowledge signatures with observed values.

22. The apparatus as recited in claim 20, further comprising at least one refinement engine, wherein the refinement engine modifies the knowledge signatures with derived values.

23. The apparatus as recited in claim 20, further comprising a distancing engine, wherein the processor performs a similarity measure between a plurality of knowledge signatures according to the distancing engine.

24. The apparatus as recited in claim 20, wherein the data-receiving devices are selected from the group consisting of audio sensors, video sensors, scanners, image sensors, optical character recognition devices, keyboards, pointing devices, network cards, modems, and combinations thereof.

25. The apparatus as recited in claim 20, wherein the datasets are selected from the group consisting of text documents, images, audio segments, video segments, database records, graphs, and combinations thereof.

26. The apparatus as recited in claim 20, further comprising a display whereon knowledge signatures are represented as graphs having concept labels on the abscissa and confidence values on the ordinate.

27. The apparatus as recited in claim 20, further comprising a communications port allowing the knowledge representation unit to interface with computational devices, said communications port supporting wired or wireless communications.

28. The apparatus as recited in claim 27, further comprising a computational device communicably connected to the knowledge representation unit, wherein the computational device receives knowledge signatures.