System and method for coalescing and representing knowledge as structured data

• US 9,977,717 B2
• Filed: 06/13/2016
• Issued: 05/22/2018
• Est. Priority Date: 03/30/2016
• Status: Active Grant

• Current Assignee: Wipro Limited
• Original Assignee: Wipro Limited
• Inventors: Adyapak, Srinivas, Hosabettu, Raghavendra
• Primary Examiner(s): Bullock, Joshua
• Application Number:
  US15/180,348
• Publication Number:
  US 20170286231A1
• Time in Patent Office: 708 Days
• Field of Search: 707625
• CPC Class Codes: G06F 11/1451 by selection of backup contents
• CPC Class Codes: G06F 16/2322 using timestamps
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

In certain embodiments, the method may comprise, determining one or more features associated with each of one or more concepts from at least one sentence; determining at least one concept feature matrix based on the one or more features; determining one or more contribution factors among the one or more concepts, based on the at least one concept feature matrix; determining incremental synchronization data by storing current timestamp associated with the one or more concepts and comparing it with knowledge data; generating at least one new concept feature matrix based on the incremental synchronization data; determining one or more new contribution factors among the one or more concepts, based on the at least one new concept feature matrix; determining differential value between the one or more contribution factors and the one or more new contribution factors; and dynamically updating the knowledge data, in the repository system.

Claims

1. A method for representing knowledge as structured data, the method comprising:

determining, by a knowledge processor, one or more features associated with each of one or more concepts from at least one sentence;

determining, by the knowledge processor, at least one concept feature matrix based on the one or more features;

determining, by the knowledge processor, one or more contribution factors among the one or more concepts, based on the at least one concept feature matrix;

determining, by the knowledge processor, incremental synchronization data by storing current timestamp associated with the one or more concepts and comparing it with knowledge data, wherein the knowledge data is pre-stored in a repository system;

generating, by the knowledge processor, at least one new concept feature matrix based on the incremental synchronization data;

determining, by the processor, one or more new contribution factors among the one or more concepts, based on the at least one new concept feature matrix;

determining, by the processor, differential value between the one or more contribution factors and the one or more new contribution factors; and

dynamically updating, by the knowledge processor, the knowledge data, in the repository system, based on the differential value.

2. The method of claim 1 wherein, the at least one sentence is identified from data received from at least one source.

3. The method of claim 2, further comprising:

decomposing data into sections, paragraphs, the at least one sentence and at least one of a phrase.

4. The method of claim 1, further comprising:

determining weight for each of the at least one feature using a weight allocation model; and

generating the at least one concept feature matrix by mapping at least one feature to its corresponding at least one concept.

5. The method of claim 1, further comprising:

generating at least one concept feature matrix.

6. The method of claim 1, wherein the one or more contribution factors are levels of knowledge contribution from the at least one concept feature matrix.

7. The method of claim 1 further comprising:

ranking the levels of knowledge contributions from at least a pair of concepts for a given set of sentences based on non-zero values in the at least one concept feature matrix.

8. The method of claim 1, wherein the incremental synchronization comprises:

semantic information and time stamps.

9. The method of claim 1, wherein the one or more new contribution factors are levels of knowledge contribution from the at least one new concept feature matrix.

10. A system for representing knowledge as structured data, comprising:

at least one processor in electronic communication with a managing device, external manufacturer applications, and a single viewer devices dashboard; and

a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to;

determining one or more features associated with each of one or more concepts from at least one sentence;

determining t least one concept feature matrix based on the one or more features;

determining one or more contribution factors among the one or more concepts, based on the at least one concept feature matrix;

determining incremental synchronization data by storing current timestamp associated with the one or more concepts and comparing it with knowledge data, wherein the knowledge data is pre-stored in a repository system;

generating at least one new concept feature matrix based on the incremental synchronization data;

determining one or more new contribution factors among the one or more concepts, based on the at least one new concept feature matrix;

determining differential value between the one or more contribution factors and the one or more new contribution factors; and

dynamically updating the knowledge data, in the repository system, based on the differential value.

11. The system of claim 10 wherein, the at least one sentence is identified from data received from at least one source.

12. The system of claim 11, further comprising:

decomposing data into sections, paragraphs, the at least one sentence and at least one of a phrase using a decomposition engine.

13. The system of claim 10, further comprising:

determining weight for each of the at least one feature using a weight allocation model; and

generating the at least one concept feature matrix by mapping at least one feature to its corresponding at least one concept.

14. The system of claim 10, further comprising:

generating at least one concept feature matrix.

15. The system of claim 10 wherein the one or more contribution factors are levels of knowledge contribution from the at least one concept feature matrix.

16. The system of claim 10 further comprising:

ranking the levels of knowledge contributions from at least a pair of concepts for a given set of sentences based on non-zero values in the at least one concept feature matrix.

17. The system of claim 10, wherein the incremental synchronization comprises:

semantic information and time stamps.

18. The system of claim 10 wherein the one or more new contribution factors are levels of knowledge contribution from the at least one new concept feature matrix.

19. A non-transitory computer-readable medium storing instructions for representing knowledge as structured data, wherein upon execution of the instructions by one or more processors, the processors perform operations comprising:

determining one or more features associated with each of one or more concepts from at least one sentence;

determining at least one concept feature matrix based on the one or more features;

determining one or more contribution factors among the one or more concepts, based on the at least one concept feature matrix;

determining incremental synchronization data by storing current timestamp associated with the one or more concepts and comparing it with knowledge data, wherein the knowledge data is pre-stored in a repository system;

generating at least one new concept feature matrix based on the incremental synchronization data;

determining one or more new contribution factors among the one or more concepts, based on the at least one new concept feature matrix;

determining differential value between the one or more contribution factors and the one or more new contribution factors; and

dynamically updating the knowledge data, in the repository system, based on the differential value.

20. The medium of claim 19 further comprising:

determining weight for each of the at least one feature using a weight allocation model; and

generating the at least one concept feature matrix by mapping at least one feature to its corresponding at least one concept.