Knowledge discovery system

• US 20050278362A1
• Filed: 02/17/2005
• Published: 12/15/2005
• Est. Priority Date: 08/12/2003
• Status: Abandoned Application

• Current Assignee: Viziant Corp.
• Original Assignee: Viziant Corp.
• Inventors: Nguyen, Bao, Perry, Dennis, Campbell, Stanley, Maren, Alianna J.
• Application Number:
  US11/059,643
• Publication Number:
  US 20050278362A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/90 Details of database functions independent of the retrieved data types
• CPC Class Codes: G06F 16/906 Clustering; Classification
• CPC Class Codes: G06N 5/025 Extracting rules from data

Abstract

A knowledge discovery apparatus and method that extracts both specifically desired as well as pertinent and relevant information to query from a corpus of multiple elements that can be structured, unstructured, and/or semi-structured, along with imagery, video, speech, and other forms of data representation, to generate a set of outputs with a confidence metric applied to the match of the output against the query. The invented apparatus includes a multi-level architecture, along with one or more feedback loop(s) from any level n to any lower level n−1 so that a user can control the output of this knowledge discovery method via providing inputs to the utility function.

Claims

1. A system for knowledge discovery from a set of structured data and/or semi-structured data and/or unstructured data elements comprising:

a first filter for filtering a first representation level of the data elements;

a first level processor for transforming the filtered data elements into a second representation level of the data elements;

a second filter for filtering the second representation of the data elements; and

a feedback controller for automatically providing feedback to one of the filters and/or the processor and/or to the first representation level of data elements based on the filtered second representation level of the data elements

2. The system of claim 1, wherein the second representation level of the data elements is at a higher level of abstraction than the first representation level of the data elements.

3. The system of claim 1, further comprising a data processor for extracting raw data elements from source data items and transforming the raw data elements into the first representation level of the data elements.

4. The system of claim 1, wherein the feedback controller modifies the first filter to control the selection of the elements of the first representation level transformed by the first processor.

5. The system of claim 1, wherein the feedback controller controls the selection or modification of a parameter for one of the filters.

6. The system of claim 1, wherein the feedback controller adjusts the first level processor to modify the transformation process from the first representation to the second representation.

7. The system of claim 1, wherein the feedback controller changes the data elements included in the first representation of data elements.

8. The system of claim 1, wherein the feedback controller includes a reasoning component for monitoring the filtered second representation of the data elements using artificial intelligence.

9. The system of claim 8, wherein the feedback controller modifies the feedback provided in order to maximize a utility function.

10. The system of claim 1, wherein the feedback controller modifies the feedback provided in order to maximize a utility function.

11. The system of claim 1, further comprising a second level processor for transforming the filtered second presentation of the data elements into a third representation level of the data elements.

12. The system of claim 1, wherein the first filter comprise a plurality of different filtering parameters.

13. The system of claim 11, wherein the feedback controller is configured to control the selection or modification of the filtering parameters.

14. A system for knowledge discovery from a corpus of structured data and/or semi-structured data and/or unstructured data elements comprising:

a first set of one or more filters applied to a first representation of the data elements, generating a subset of the first representation data elements, wherein the filters are configured to employ a first set of criteria to determine filter selection and filter parameters governing data element subset selection;

a first level processor configured to execute one or more processing methods for transforming the selected subset of the first representation of the data elements into a second representation level;

a second set of one or more filters applied to a second representation of the data elements, generating a subset of the second representation data elements, wherein the second set of filters are configured to employ a second set of criteria to determine filter selection and filter parameters governing data element subset selection;

a second level processor configured to execute one or more processing methods for transforming a subset of the second representation level of the data elements into a third representation having a higher abstraction than the first and second representation levels.

15. The system of claim 14, further comprising:

a third set of one or more filters applied to a second representation of the data elements, generating a proper subset of the third representation data elements, wherein the filters are configured to employ a third set of criteria to determine filter selection and filter parameters governing data element subset selection; and

a third level processor configured to execute a set of one or more processing methods for identifying and characterizing relationships between the third representation of the data elements and for producing a fourth representation of data elements containing information relating to the relationship between the elements contained in the third representation.

16. The system of claim 14, wherein each of the processors is configured to include a traceability feature so that the relationships between the data elements can be identified using the data elements as found in the prior representation levels, including traceback to source data items.

17. The system of claim 14, wherein one of the representations includes concept classification.

18. The system of claim 17, wherein one of the representation levels higher than the representation that includes concept classification includes concept-to-concept association.

19. The system of claim 18, wherein one of the representation levels higher than the representation that includes concept-to-concept association includes relationship identification between associated concepts.

20. The system of claim 18, wherein one of the representation levels higher than the representation that includes concept-to-concept association includes full syntactic and/or structural analysis of either or both complete or partial segments the source data items generating those concepts represented at the level of concept-to-concept association.

21. The system of claim 14, further comprising a feedback controller for modifying the transformation process being performed by one of the processors and/or for modifying filter selection and filter parameter determination and/or for modifying one of the representations of the data.

22. The system of claim 21, wherein the feedback controller operates to maximize a utility function.

23. The system of claim 21, wherein the feedback controller includes a reasoning component configured to monitor the representations of the data being formed by the processors.

24. A system for knowledge discovery from a corpus of structured data and/or semi-structured data and/or unstructured data elements comprising:

a first level processor for transforming a subset of a first representation of the data elements into a second representation;

a feedback controller for modifying the transformation process performed by the first level processor based on the contents of the second representation and a utility function.

25. The system of claim 24, wherein the feedback controller is configured to modify the transformation process in order to maximize the utility function.

26. The system of claim 24, wherein the feedback controller includes a reasoning component.

27. The system of claim 24, wherein the reasoning component utilizes artificial intelligence.

28. The system of claim 24, wherein the feedback controller is configured to modify the subset of the first representation of data elements being transformed by the first level processor.

29. The system of claim 24, wherein the system includes a filter having a plurality of different filtering parameters for creating the subset of the first representation of the data elements.

30. The system of claim 29, wherein the feedback controller is configured to control the selection or modification of the filtering parameters.

31. The system of claim 24, wherein the feedback controller changes the data elements included in the subset of the first representation of data elements.

32. The system of claim 24, further comprising a filter for creating a subset of the second representation of the data elements.

33. The system of claim 32, wherein the feedback controller includes a reasoning component for monitoring the filtered second representation of the data elements using artificial intelligence.

34. The system of claim 32, further comprising a second level processor for transforming the filtered second representation of the data elements into a third representation level of the data elements.

35. The system of claim 24, further comprising a data processor for extracting raw data elements from source data items and transforming the raw data elements into the first representation level of the data elements.

36. A system for knowledge discovery from a corpus of structured data and/or semi-structured data and/or unstructured data elements comprising:

a first level processor for transforming a subset of a first representation of the data elements from the corpus into a second representation having a higher abstraction than the first representation,

wherein the first level processor is configured to map the second representation of the data elements in a many-to-many manner to a predetermined taxonomy containing nodes in a many-to-many manner;

a feedback controller including a reasoning component configured to monitor the second representation of data elements and to identify the population of the data in the second representation towards the taxonomy as defined by the various many-to-many mappings between the data elements in the second representation and the nodes in the predetermined taxonomy.

37. The system of claim 36, wherein the feedback controller is configured to monitor metrics regarding how the second representation of the data populates toward the taxonomy.

38. The system of claim 36, wherein the feedback controller provides a feedback control signal to the first level processor in order to direct the transformation of the subset of the first representation of the data elements.

39. The system of claim 38, further comprising a filter for creating the subset of the first representation of data elements and wherein the feedback control signal contains instructions relating to the selection of filter parameters to be applied to the first representation of the data elements.

40. The system of claim 36, wherein the feedback controller provides feedback to the first level processor in order to adapt the algorithmic methodology by which the elements of the second representation populate to the taxonomy.

41. The system of claim 37, wherein the feedback controller is configured to monitor the extent to which a given node within the taxonomy potentially is mapped towards by more than one distinct combination of data elements at the second representation level.

42. The system of claim 36, wherein the feedback controller is configured to automatically adapt the predetermined taxonomic structure to include additional nodes in order to distinguish between combinations of data elements in the second representation.

43. The system of claim 36, wherein the feedback controller is configured to adapt the predetermined taxonomic structure to include additional nodes;

and wherein the first level processor is configured to map multiple distinct combinations of data elements to a first node in the predetermined taxonomic structure and also map the distinct combinations of data elements to the additional nodes in a manner that distinguishes between the multiple distinct combinations while maintaining the mapping to the nodes in the predetermined taxonomy.

44. A system for knowledge discovery of structured data and/or semi-structured data and/or unstructured data comprising:

wherein the data is represented in at least two different representation modalities; and

wherein a separate system for processing each representation modality exists; and

wherein each separate processing system includes a first level processor for transforming the data from a first representation level of data elements into a second representation level having a higher level of abstraction than the first representation level, and

wherein the two processing systems share a common a feedback controller for automatically controlling each of the first level processors based on the contents of the respective second representation level;

wherein the feedback controller is configured to control one of the processing systems based on the data elements represented in the other of the processing systems.

45. The system of claim 44, wherein each of the processing systems includes a second level processor for transforming data from the second representation level into a third representation level.