Universal cognitive graph having persistent knowledge elements

• US 10,860,934 B2
• Filed: 10/11/2016
• Issued: 12/08/2020
• Est. Priority Date: 05/13/2016
• Status: Active Grant

• Current Assignee: Tecnotree Technologies Incorporated
• Original Assignee: Cognitive Scale Inc (Tecnotree Oyj)
• Inventors: Lindsley, Hannah R.
• Primary Examiner(s): Shmatov, Alexey
• Assistant Examiner(s): Nguyen, Tri T
• Application Number:
  US15/290,485
• Publication Number:
  US 20170330094A1
• Time in Patent Office: 1,519 Days
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 16/84 Mapping; Conversion
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards
• CPC Class Codes: G06N 5/048 Fuzzy inferencing

Abstract

A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element of the collection of knowledge elements being persisted in its original form.

Claims

1. A system comprising:

a processor;

a data bus coupled to the processor; and

a non-transitory, computer-readable storage medium embodying computer program code for managing a cognitive graph of a universal knowledge repository, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for;

receiving data from a plurality of data sources;

processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and

,

storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the cognitive graph comprising integrated machine learning functionality, the machine learning functionality including cognitive functionality, the cognitive functionality using feedback to improve accuracy of knowledge stored within the cognitive graph, the cognitive graph seamlessly functioning with a cognitive inference and learning system, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a plurality of agents, the plurality of agents comprising an enrichment agent and an insight agent, the enrichment agent performing enrichment operations on the data from the plurality of data sources, results of the enrichment operations being stored within the cognitive graph, the insight agent interacting with the cognitive graph to generate a cognitive insight, the storing universally representing knowledge obtained from the data, each knowledge element of the collection of knowledge elements being persisted in its original form, persisting a knowledge element in its original form comprises once a knowledge element is stored within the cognitive graph, the knowledge element is not deleted, overwritten or modified;

performing mapping operations on a query to generate query related knowledge elements, the mapping operations generating a set of parse trees using a parse rule set, the mapping operations comprising mapping structural elements to resolve ambiguity, the mapping operations comprising mapping structural elements of the query around a verb of the query, the mapping of the structural elements transforming the structural elements into words higher up an inheritance chain within the cognitive graph, the parse trees being ranked by a conceptualization ranking rule set, the parse trees representing ambiguous portions of the text of the query;

performing a conceptualization operation, the conceptualization operation identifying relationships of concepts identified from ranking the set of parse trees using the conceptualization ranking rule set, the conceptualization operations generating a set of conceptualization ambiguity options, the set of conceptualization ambiguity options being ranked using the conceptualization ranking rule set, top-ranked conceptualization options being stored in the cognitive graph;

submitting an insight agent query from the insight agent to the universal knowledge repository; and

,

providing matching results to the insight agent responsive to the insight agent query based upon a matching rule set and a plurality of answer related knowledge elements in the universal knowledge repository.

2. The system of claim 1, wherein the instructions executable by the processor further comprise instructions for:

generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph.

3. The system of claim 1, wherein:

when a first knowledge element from the collection of knowledge elements conflicts with a second knowledge element from the collection of knowledge elements, both of the first knowledge element and the second knowledge element are stored within the cognitive graph.

4. The system of claim 3, wherein:

the first knowledge element comprises a first data source property and a first time stamp property;

the second knowledge element comprises a second data source property and a second time stamp property; and

,

the first knowledge element and the second knowledge element comprise characteristics of at least one of;

the first knowledge element is sourced from a first data source with an associated first time stamp is contradicted by the second knowledge element sourced from a second data source and associated with a second time stamp;

the first knowledge element sourced from the first data source with the associated first time stamp is contradicted by the second knowledge element sourced from the first data source and associated with a second time stamp;

the first knowledge element is sourced from a first data source with an associated first time stamp is contradicted by the second knowledge element sourced from a second data source and associated with a second time stamp, the associated first time stamp and associated second time stamp being substantially equivalent.

5. The system of claim 3, wherein:

a first knowledge element is found to be incorrect as a result of an addition of a second knowledge element being stored within the cognitive graph relating to a same subject.

6. A non-transitory, computer-readable storage medium embodying computer program code for managing a cognitive graph of a universal knowledge repository, the computer program code comprising computer executable instructions configured for:

receiving data from a plurality of data sources;

processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and

,

storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the cognitive graph comprising integrated machine learning functionality, the machine learning functionality including cognitive functionality, the cognitive functionality using feedback to improve accuracy of knowledge stored within the cognitive graph, the cognitive graph seamlessly functioning with a cognitive inference and learning system, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a plurality of agents, the plurality of agents comprising an enrichment agent and an insight agent, the enrichment agent performing enrichment operations on the data from the plurality of data sources, results of the enrichment operations being stored within the cognitive graph, the insight agent interacting with the cognitive graph to generate a cognitive insight, the storing universally representing knowledge obtained from the data, each knowledge element of the collection of knowledge elements being persisted in its original form, persisting a knowledge element in its original form comprises once a knowledge element is stored within the cognitive graph, the knowledge element is not deleted, overwritten or modified;

performing mapping operations on a query to generate query related knowledge elements, the mapping operations generating a set of parse trees using a parse rule set, the mapping operations comprising mapping structural elements to resolve ambiguity, the mapping operations comprising mapping structural elements of the query around a verb of the query, the mapping of the structural elements transforming the structural elements into words higher up an inheritance chain within the cognitive graph, the parse trees being ranked by a conceptualization ranking rule set, the parse trees representing ambiguous portions of the text of the query;

performing a conceptualization operation, the conceptualization operation identifying relationships of concepts identified from ranking the set of parse trees using the conceptualization ranking rule set, the conceptualization operations generating a set of conceptualization ambiguity options, the set of conceptualization ambiguity options being ranked using the conceptualization ranking rule set, top-ranked conceptualization options being stored in the cognitive graph;

submitting an insight agent query from the insight agent to the universal knowledge repository; and

,

providing matching results to the insight agent responsive to the insight agent query based upon a matching rule set and a plurality of answer related knowledge elements in the universal knowledge repository.

7. The non-transitory, computer-readable storage medium of claim 6, wherein the instructions executable by the processor further comprise instructions for:

generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph.

8. The non-transitory, computer-readable storage medium of claim 6, wherein the instructions executable by the processor further comprise instructions for:

when a first knowledge element from the collection of knowledge elements conflicts with a second knowledge element from the collection of knowledge elements, both of the first knowledge element and the second knowledge element are stored within the cognitive graph.

9. The non-transitory, computer-readable storage medium of claim 8, wherein:

the first knowledge element comprises a first data source property and a first time stamp property;

the second knowledge element comprises a second data source property and a second time stamp property; and

,

the first knowledge element and the second knowledge element comprise characteristics of at least one of;

the first knowledge element is sourced from a first data source with an associated first time stamp is contradicted by the second knowledge element sourced from a second data source and associated with a second time stamp;

the first knowledge element sourced from the first data source with the associated first time stamp is contradicted by the second knowledge element sourced from the first data source and associated with a second time stamp;

the first knowledge element is sourced from a first data source with an associated first time stamp is contradicted by the second knowledge element sourced from a second data source and associated with a second time stamp, the associated first time stamp and associated second time stamp being substantially equivalent.

10. The non-transitory, computer-readable storage medium of claim 8, wherein

a first knowledge element is found to be incorrect as a result of an addition of a second knowledge element being stored within the cognitive graph relating to a same subject.

11. The non-transitory, computer-readable storage medium of claim 6, wherein the computer executable instructions are deployable to a client system from a server system at a remote location.

12. The non-transitory, computer-readable storage medium of claim 6, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.