Orchestrated hydration of a knowledge graph

• US 10,614,086 B2
• Filed: 11/21/2018
• Issued: 04/07/2020
• Est. Priority Date: 07/06/2018
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Tung, Teresa Sheausan, Puri, Colin Anil, Wang, Zhijie, Kujawinski, Matthew Bernard
• Primary Examiner(s): Trujillo, James
• Assistant Examiner(s): Morris, John J
• Application Number:
  US16/198,237
• Publication Number:
  US 20200012738A1
• Time in Patent Office: 503 Days
• Field of Search: None
• CPC Class Codes: G06F 16/211 Schema design and management
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence

Abstract

A knowledge data management system (KDMS) implements data refinement orchestration, resolution, and refinement to provide a reusable and generic solution to ingest and link data via relationships and properties when constructing a knowledge graph. The KDMS thus operates to break down existing data storage silos by normalizing and integrating data with a uniform semantic schema, which results in more accurate and faster knowledge graph construction.

Claims

1. A system comprising:

knowledge extraction circuitry configured to;

receive a search query;

parse a database comprising historical data; and

extract, from the database, a data set of intermediary results corresponding to the search query;

processing circuitry configured to;

receive the data set of intermediary results;

provide the intermediary results to a staging repository;

receive, from a supplemental pipeline data source, a supplemental data set of additional intermediary results;

provide the supplemental data set of additional intermediary results to the staging repository;

iteratively refine, using the staging repository, the data set of intermediary results with the additional intermediary results for inclusion in a knowledge graph into subsequent levels of one or more additional sub-concepts until a condition for ceasing further refinement is satisfied;

generate, based on the iterative refinement, a refined data set comprising refined intermediary results; and

store the refined data set in the staging repository;

integration circuitry configured to;

orchestrate, from the refined data set, a knowledge data set to synchronize with a knowledge graph schema;

resolve, within the knowledge data set, instances of different expressions of a same entity;

reconcile the knowledge data set to existing knowledge profiles; and

perform schema management; and

knowledge graph circuitry configured to;

generate the knowledge graph to be a visual representation of the knowledge data set, wherein the knowledge graph includes nodes and connecting edges that represent entity and relationship information, respectively, included in the knowledge data set.

2. The system of claim 1, wherein the processing circuitry is further configured to:

normalize data comprising the data set into a common data format.

3. The system of claim 1, wherein the integration circuitry is configured to execute an algorithm with contextual information to resolve the instances of different expressions of the same entity.

4. The system of claim 3, wherein the algorithm includes at least one of a similarity algorithm, a classification algorithm, a detection algorithm, or a recognition algorithm.

5. The system of claim 1, wherein the integration circuitry is configured to reconcile the knowledge data to revise the knowledge data set to a target schema.

6. The system of claim 1, wherein the schema management includes at least one of graph versioning, namespacing, concept hierarchy, schema migration, or graph pruning.

7. The system of claim 1, wherein the knowledge extraction circuitry is configured to extract the data set from a plurality of data source pipelines.

8. The system of claim 7, wherein the plurality of data source pipelines includes at least two or more of online data, an updating newsfeed, or an enterprise database.

9. The system of claim 1, wherein the knowledge extraction circuitry is configured to extract the data set according to an applied natural language processing (NLP), a structured data denormalization and normalization, or a computer vision/object detection and recognition technique.

10. The system of claim 1, wherein the integration circuitry is configured to orchestrate the knowledge data set from the refined data set by further refining the refined data set into an additional sub concept.

11. The system of claim 1, wherein the knowledge graph schema is stored in a graph repository including graph entity types, graph edge types, and corresponding graph hierarchies.

12. A method comprising:

receiving, by a knowledge extraction circuitry, a search query;

parsing, by the knowledge extraction circuitry, a database comprising historical data;

extracting, by the knowledge extraction circuitry, a data set of intermediary results corresponding to the search query from the database;

receiving, by a processing circuitry, the data set of intermediary results;

providing, by the processing circuitry, the intermediary results to a staging repository;

receiving, by the processing circuitry, a supplemental data set of additional intermediary results from a supplemental pipeline data source;

providing, by the processing circuitry, the supplemental data set of additional intermediary results to the staging repository;

iteratively refining, by the processing circuitry, the data set of intermediary results with the additional intermediary results for inclusion in a knowledge graph using the staging repository, wherein the data set of intermediary results is iteratively refined into subsequent levels of one or more additional sub-concepts until a condition for ceasing further refinement is satisfied;

generating, by the processing circuitry, a refined data set comprising refined intermediary results based on the iterative refinement; and

storing, by the processing circuitry, the refined data set in the staging repository;

orchestrating, by an integration circuitry, a knowledge data set from the refined data set, wherein the knowledge data set synchronizes with a knowledge graph schema;

resolving, by the integration circuitry, instances within the knowledge data set of different expressions of a same entity;

reconciling, by the integration circuitry, the knowledge data set to existing knowledge profiles;

performing, by the integration circuitry, schema management; and

generating, by a knowledge graph circuitry, a knowledge graph to be a visual representation of the knowledge data set, wherein the knowledge graph includes nodes and connecting edges that represent entity and relationship information, respectively, included in the knowledge data set.

13. The method of claim 12, further comprising:

normalizing, by the processing circuitry, data comprising the data set into a common data format.

14. The method of claim 12, wherein resolving instances within the knowledge data set of different expressions of a same entity comprises executing an algorithm with contextual information to resolve the instances of different expressions of the same entity;

and

wherein the algorithm includes at least one of a similarity algorithm, a classification algorithm, a detection algorithm, or a recognition algorithm.

15. The method of claim 12, wherein reconciling the knowledge data set comprises revising the knowledge data set to a target schema to reconcile the knowledge data set.

16. The method of claim 12, wherein the schema management includes at least one of graph versioning, namespacing, concept hierarchy, schema migration, or graph pruning.

17. The method of claim 12, wherein the data set is extracted from a plurality of data source pipelines including at least two or more of online data, an updating newsfeed, or an enterprise database.

18. A system comprising:

a machine-readable medium, other than a transitory signal; and

instructions stored on the machine-readable medium that, when executed by processing circuitry, cause a computing device to;

receive a search query;

parse a database comprising historical data;

extract, from the database, a data set of intermediary results corresponding to the search query;

receive the data set of intermediary results;

provide the intermediary results to a staging repository;

receive, from a supplemental pipeline data source, a supplemental data set of additional intermediary results;

provide the supplemental data set of additional intermediary results to the staging repository;

iteratively refine, using the staging repository, the data set of intermediary results with the additional intermediary results for inclusion in a knowledge graph into subsequent levels of one or more additional sub-concepts until a condition for ceasing further refinement is satisfied;

generate, based on the iterative refinement, a refined data set comprising refined intermediary results; and

store the refined data set in the staging repository;

orchestrate, from the refined data set, a knowledge data set to synchronize with a knowledge graph schema;

resolve, within the knowledge data set, instances of different expressions of a same entity;

reconcile the knowledge data set to existing knowledge profiles;

perform schema management; and

generate the knowledge graph to be a visual representation of the knowledge data set, wherein the knowledge graph includes nodes and connecting edges that represent entity and relationship information, respectively, included in the knowledge data set.

19. The system of claim 18, wherein the instructions, when executed by the processing circuitry, further cause the computing device to:

normalize data comprising the data set into a common data format.

20. The system of claim 18, wherein the schema management includes at least one of graph versioning, namespacing, concept hierarchy, schema migration, or graph pruning.