Machine learning to integrate knowledge and natural language processing

Patent metadata

Field	Value
Publication number	US-10423726-B2
Application number	US-201815866698-A
Country	US
Kind code	B2
Filing date	Jan 10, 2018
Priority date	Jan 10, 2018
Publication date	Sep 24, 2019
Grant date	Sep 24, 2019

Abstract

A system, computer program product, and method are provided to automate a framework for knowledge graph based persistence of data, and to resolve temporal changes and uncertainties in the knowledge graph. Natural language understanding, together with one or more machine learning models (MLMs), is used to extract data from unstructured information, including entities and entity relationships. The extracted data is populated into a knowledge graph. As the KG is subject to change, the KG is used to create new and retrain existing machine learning models (MLMs). Weighting is applied to the populated data in the form of veracity value. Blockchain technology is applied to the populated data to ensure reliability of the data and to provide auditability to assess changes to the data.

First claim

What is claimed is:

1. A computer system comprising:
a processing unit operatively coupled to memory;
an artificial intelligence platform, in communication with the processing unit and memory;
a knowledge engine in communication with the processing unit to manage data, including:
extract data and a data relationship from data selected from the group consisting of: structured data, unstructured data, and combinations thereof;
create an entry for the extracted data and data relationship in a knowledge graph (KG) and selectively store the extracted data and data relationship in the KG, including assign a veracity value to the stored data;
create an asset value entry in a blockchain (BC) ledger corresponding to the KG, the entry including the assigned veracity value;
create a BC identifier corresponding to the BC ledger entry; and
store the created BC identifier with the KG entry;
evaluate select data stored in the KG, including employ the BC identifier to determine provenance of the select data and to quantify the data; and
generate a list of the evaluated data, and sort the data in the generated list based on the assigned veracity value; and

a data element returned from the sorted list with a strongest veracity score.

2. The system of claim 1 , further comprising the knowledge engine to:
create a first partition within the KG and populate and assign a first reliability value to first data in the first partition;
create a second partition within the KG and populate and assign a second reliability value to second data in the second partition, wherein the first and second reliability values are different.

3. The system of claim 2 , further comprising the knowledge engine to automatically perform a veracity evaluation within the KG, including comparison of the first and second data.

4. The system of claim 1 , further comprising the knowledge engine to:
establish a link between two knowledge graphs, including compare and evaluate data elements in a second KG with data elements in a first KG, and selectively replace data elements based on a value selected from the group consisting of: reliability, feedback, and combinations thereof.

5. The system of claim 4 , further comprising the knowledge engine to maintain a structure of the KG constant following establishment of the link between the first KG and the second KG.

6. The system of claim 1 , wherein the data is stored in a node in the KG and the relationship is represented as an edge connecting two nodes, each node having a node level veracity value and each relationship having a relationship veracity value, wherein the relationship value is calculated based on the veracity values of the nodes in the relationship.

7. A computer program product to process natural language, the computer program product comprising a computer readable storage device having program code embodied therewith, the program code executable by a processing unit to:
store data in a knowledge graph (KG), comprising:
extract data and a data relationship from data selected from the group consisting of: structured data, unstructured data, and combinations thereof;
create an entry in the KG and selectively store the extracted data and data relationship in the KG, including assign a veracity value to the stored data;
create an asset value entry in a blockchain (BC) ledger corresponding to the KG, the entry including the assigned veracity value;
create a BC identifier corresponding to the BC ledger entry; and
store the created BC identifier with the KG entry;

evaluate select data stored in the KG, including employ the BC identifier to determine provenance of the select data and to quantify the data;
generate a list of the evaluated data, and sort the data in the generated list based on the assigned veracity value; and
generate an outcome, wherein the outcome is a data element returned from the sorted list with a strongest veracity score.

8. The computer program product of claim 7 , further comprising program code to:
create a first partition within the KG and populate and assign a first reliability value to first data in the first partition;
create a second partition within the KG and populate and assign a second reliability value to second data in the second partition, wherein the first and second reliability values are different.

9. The computer program product of claim 8 , further comprising program code to automatically perform a veracity evaluation within the KG, including comparison of the first and second data.

10. The computer program product of claim 7 , further comprising program code to:
establish a link between two knowledge graphs, including compare and evaluate data elements in a second KG with data elements in a first KG, and selectively replace data elements based on a value selected from the group consisting of: reliability, feedback, and combinations thereof.

11. The computer program product of claim 10 , further comprising program code to maintain a structure of the KG constant following establishment of the link between the first KG and the second KG.

12. The computer program product of claim 7 , wherein the data is stored in a node in the KG and the relationship is represented as an edge connecting two nodes, each node having a node level veracity value and each relationship having a relationship veracity value, wherein the relationship value is calculated based on the veracity values of the nodes in the relationship.

13. A method for processing natural language, comprising:
storing data in a knowledge graph (KG), comprising:
extracting data and a data relationship from data selected from the group consisting of: structured data, unstructured data, and combinations thereof;
creating an entry in the KG and selectively storing the extracted data and data relationship in the KG, including assigning a veracity value to the stored data;
creating an asset value entry in a blockchain (BC) ledger corresponding to the KG, the entry including the assigned veracity value;
creating a BC identifier corresponding to the BC ledger entry; and
storing the created BC identifier with the KG entry;

evaluating select data stored in the KG, including employing the BC identifier to determine provenance of the select data and to quantify the data;
generating a list of the evaluated data, and sorting the data in the generated list based on the assigned veracity value; and
a data element returned from the sorted list with a strongest veracity score.

14. The method of claim 13 , further comprising
creating a first partition within the KG and populating and assigning a first reliability value to first data in the first partition;
creating a second partition within the KG and populating and assigning a second reliability value to second data in the second partition, wherein the first and second reliability values are different.

15. The method of claim 14 , further comprising automatically performing a veracity evaluation within the KG, including comparison of the first and second data.

16. The method of claim 13 , further comprising:
establishing a link between two knowledge graphs, including comparing and evaluating data elements in a second KG with data elements in a first KG, and selectively replacing data elements based on a value selected from the group consisting of: reliability, feedback, and combinations thereof.

17. The method of claim 16 , further comprising maintaining a structure of the KG constant following establishment of the link between the first KG and the second KG.

18. The method of

Assignees

• IBM

Inventors

• Bacarella David
• Barnebee Iv James H
• Lawrence Nicholas
• Patel Sumit

Classifications

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F40/226

  Validation · CPC title

• G06F40/216

  using statistical methods · CPC title

• G06F40/30

  Semantic analysis · CPC title