Machine learning to integrate knowledge and natural language processing

• US 10,599,780 B2
• Filed: 06/21/2019
• Issued: 03/24/2020
• Est. Priority Date: 01/10/2018
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bacarella, David, Barnebee, IV, James H., Lawrence, Nicholas, Patel, Sumit
• Primary Examiner(s): Hoque, Nafiz E
• Application Number:
  US16/448,159
• Publication Number:
  US 20190303441A1
• Time in Patent Office: 277 Days
• CPC Class Codes: G06F 16/3331 Query processing
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/226 Validation
• CPC Class Codes: G06F 40/237 Lexical tools
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards
• CPC Class Codes: H04L 67/104 Peer-to-peer [P2P] networks
• CPC Class Codes: H04L 9/0637 Modes of operation, e.g. cipher block chaining [CBC], electronic codebook [ECB] or Galois/counter mode [GCM]
• CPC Class Codes: H04L 9/3239 involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD
• CPC Class Codes: H04L 9/50 using hash chains, e.g. blockchains or hash trees

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 and a data relationship from structured and/or unstructured data, create an entry in the KG and selectively store the extracted data and data relationship in the KG, assign a veracity value to the stored data, create an asset value entry in a corresponding BC ledger, and store a BC identifier with the KG entry.

Claims

1. A computer system comprising:

a processing unit operatively coupled to memory;

an artificial intelligence (AI) platform, in communication with the processing unit and memory 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;

store a BC identifier corresponding to the BC ledger entry with the KG entry; and

return a data element with a corresponding veracity score.

2. The system of claim 1, further comprising the AI platform to:

partition data within the KG to create first and second partitions, and

populate and assign a first reliability value to first data in the first partition and 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 1, further comprising the AI platform 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.

4. 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.

5. The system of claim 1, further comprising the AI platform to:

extract data and a data relationship from a first KG, wherein the extracted data and data relationship from the first KG has a veracity value stored in a corresponding BC ledger;

extract data and a data relationship from a second KG, wherein the extracted data and data relationship from the second KG has a veracity value stored in the BC ledger;

evaluate the veracity value of the extracted data and data relationship from the first KG with the veracity value of the extracted data and data relationship from the second KG, the evaluation including an assessment of the veracity values to identify a modification to content in the first KG; and

modify a machine learning model (MLM) associated with the first KG based on the assessment.

6. The system of claim 5, further comprising the AI platform to:

search the KG and identify new data and data relationships;

generate a list of the identified new data and data relationships present in the KG and absent from the machine learning model (MLM);

update the MLM with the generated list of data and data relationships; and

store the updated MLM in a MLM library as a new MLM.

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:

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;

store a BC identifier corresponding to the BC ledger with the KG entry; and

return a data element with a corresponding veracity score.

8. The computer program product of claim 7, further comprising program code to:

partition data within the KG to create first and second partitions, and

populate and assign a first reliability value to first data in the first partition and 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 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.

10. 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.

11. The computer program product of claim 7, further comprising program code to:

extract data and a data relationship from a first KG, wherein the extracted data and data relationship from the first KG has a veracity value stored in a corresponding BC ledger;

extract data and a data relationship from a second KG, wherein the extracted data and data relationship from the second KG has a veracity value stored in the BC ledger;

evaluate the veracity value of the extracted data and data relationship from the first KG with the veracity value of the extracted data and data relationship from the second KG, the evaluation including an assessment of the veracity values to identify a modification to content in the first KG; and

modify a machine learning model (MLM) associated with the first KG based on the assessment.

12. The computer program product of claim 11, further comprising program code to:

search the KG and identify new data and data relationships;

generate a list of the identified new data and data relationships present in the KG and absent from the machine learning model (MLM);

update the MLM with the generated list of data and data relationships; and

store the updated MLM in a MLM library as a new MLM.

13. A method for processing natural language, 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 for the extracted data and data relationship in a knowledge graph (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;

storing a BC identifier corresponding to the BC ledger entry with the KG entry; and

returning a data element with a corresponding veracity score.

14. The method of claim 13, further comprising:

partitioning data within the KG to create first and second partitions, and

populating and assigning a first reliability value to first data in the first partition and 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 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.

16. The method of claim 13, 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.

17. The method of claim 13, further comprising:

extracting data and a data relationship from a first KG, wherein the extracted data and data relationship from the first KG has a veracity value stored in a corresponding BC ledger;

extracting data and a data relationship from a second KG, wherein the extracted data and data relationship from the second KG has a veracity value stored in the BC ledger;

evaluating the veracity value of the extracted data and data relationship from the first KG with the veracity value of the extracted data and data relationship from the second KG, the evaluation including an assessment of the veracity values to identify a modification to content in the first KG; and

modifying a machine learning model (MLM) associated with the first KG based on the assessment.

18. The method of claim 17, further comprising:

searching the KG and identifying new data and data relationships;

generating a list of the identified new data and data relationships present in the KG and absent from the machine learning model (MLM);

updating the MLM with the generated list of data and data relationships; and

storing the updated MLM in a MLM library as a new MLM.