Linking entities in dynamic graphs

Patent metadata

Field	Value
Publication number	US-11036718-B2
Application number	US-201816117128-A
Country	US
Kind code	B2
Filing date	Aug 30, 2018
Priority date	Aug 30, 2018
Publication date	Jun 15, 2021
Grant date	Jun 15, 2021

Abstract

Implementations of the present disclosure are generally directed to linking entities in dynamic knowledge graphs, and more particularly, to linking entities in knowledge graphs based on contextual evolution in one or more dimensions.

First claim

What is claimed is:

1. A computer-implemented method executed by one or more processors, the method comprising:
receiving an initial knowledge graph;
receiving a database of changes, each change corresponding to a dimension of one or more dimensions;
generating a set of linked knowledge graphs for a particular domain, the set of linked knowledge graphs representing an evolution from the initial knowledge graph in the one or more dimensions, each knowledge graph in the set of linked knowledge graphs being based on one or more changes provided in the database of changes and the initial knowledge graph, the set of linked knowledge graphs being generated by:
providing a link between a node of the initial knowledge graph and a node of a first knowledge graph of the set, and providing a link between the node of the first knowledge graph and a node of a corresponding second knowledge graph of the set, a value of the node of the first knowledge graph being different from a value of the node of the second knowledge graph, the first knowledge graph and the second knowledge graph representing respective versions of the initial knowledge graph, and links being absent between nodes of the initial knowledge graph and the first knowledge graph and nodes of the first knowledge graph and the second knowledge graph having values that are consistent between versions;

receiving query data from a data source;
generating a query knowledge graph based on the query data and one of the first knowledge graph and the second knowledge graph in the set of linked knowledge graphs, the query knowledge graph comprising a change with respect to the one of the first knowledge graph and the second knowledge graph in the set of linked knowledge graphs; and
determining that the query data is inconsistent with the query knowledge graph, and in response, issuing an alert.

2. The method of claim 1 , wherein the query knowledge graph is generated based on the query data, and a final knowledge graph in the set of linked knowledge graphs.

3. The method of claim 1 , wherein determining that the context data is inconsistent with the reconfigured knowledge graph at least partially comprises determining that the link is invalid.

4. The method of claim 1 , wherein the context data changes in at least one dimension of the one or more dimensions.

5. The method of claim 1 , wherein the one or more dimensions comprise time, geography, and regulations.

6. The method of claim 1 , wherein generating a disambiguated domain ontology based on a domain ontology of the particular domain, and context data received from a data source comprises linking a term of the domain ontology with a respective value of the context data.

7. The method of claim 1 , wherein generating a contextualized knowledge graph based on the disambiguated domain ontology, and a knowledge graph in the set of linked knowledge graphs comprises associating a term of the disambiguated domain ontology with a node of the knowledge graph, and the change comprising an added node to the contextualized knowledge graph from the knowledge graph.

8. The method of claim 1 , wherein at least a portion of the context data is received from one or more sensors.

9. The method of claim 1 , wherein the query knowledge graph is generated by inserting one or more nodes based on the query data into the knowledge graph in the set of linked knowledge graphs.

10. The method of claim 9 , further comprising linking the one or more inserted nodes to one or more nodes in the knowledge graph in the set of linked knowledge graphs.

11. The method of claim 10 , further comprising determining the node in the knowledge graph in the set of linked knowledge graphs to which the inserted node is to be linked based on context data received from a data source.

12. The method of claim 1 , wherein the query data varies over the dimension.

13. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving an initial knowledge graph;
receiving a database of changes, each change corresponding to a dimension of one or more dimensions;
generating a set of linked knowledge graphs for a particular domain, the set of linked knowledge graphs representing an evolution from the initial knowledge graph in the one or more dimensions, each knowledge graph in the set of linked knowledge graphs being based on one or more changes provided in the database of changes and the initial knowledge graph, the set of linked knowledge graphs being generated by:
providing a link between a node of the initial knowledge graph and a node of a first knowledge graph of the set, and providing a link between the node of the first knowledge graph and a node of a corresponding second knowledge graph of the set, a value of the node of the first knowledge graph being different from a value of the node of the second knowledge graph, the first knowledge graph and the second knowledge graph representing respective versions of the initial knowledge graph, and links being absent between nodes of the initial knowledge graph and the first knowledge graph and nodes of the first knowledge graph and the second knowledge graph having values that are consistent between versions;

receiving query data from a data source;
generating a query knowledge graph based on the query data and one of the first knowledge graph and the second knowledge graph in the set of linked knowledge graphs, the query knowledge graph comprising a change with respect to the one of the first knowledge graph and the second knowledge graph in the set of linked knowledge graphs; and
determining that the query data is inconsistent with the query knowledge graph, and in response, issuing an alert.

14. The computer-readable storage medium of claim 13 , wherein the query knowledge graph is generated based on the query data, and a final knowledge graph in the set of linked knowledge graphs.

15. The computer-readable storage medium of claim 13 , wherein determining that the context data is inconsistent with the reconfigured knowledge graph at least partially comprises determining that the link is invalid.

16. The computer-readable storage medium of claim 13 , wherein the context data changes in at least one dimension of the one or more dimensions.

17. The computer-readable storage medium of claim 13 , wherein the one or more dimensions comprise time, geography, and regulations.

18. The computer-readable storage medium of claim 13 , wherein generating a disambiguated domain ontology based on a domain ontology of the particular domain, and context data received from a data source comprises linking a term of the domain ontology with a respective value of the context data.

19. The computer-readable storage medium of claim 13 , wherein generating a contextualized knowledge graph based on the disambiguated domain ontology, and a knowledge graph in the set of linked knowledge graphs comprises associating a term of the disambiguated domain ontology with a node of the knowledge graph, and the change comprising an added node to the contextualized knowledge graph from the knowledge graph.

20. The computer-readable storage medium of claim 13 , wherein at least a portion of the context data is received from one or more sensors.

21. The computer-readable storage medium of claim 13 , wherein the query knowledge graph is generated by insertin

Assignees

• Accenture Global Solutions Ltd

Inventors

• Lecue Freddy
• Zaman Md Faisal

Classifications

• G06F16/2365 Primary

  Ensuring data consistency and integrity · CPC title

• G06F16/245

  Query processing · CPC title

• G06F16/9024

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title