Weighted cognitive personas and profiles

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, 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 streams of data from a plurality of data sources;

processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph, the data enriching performing sentiment analysis, geotagging and entity detection operations on the streams of data from the plurality of data sources, the processing being performed by a cognitive inference and learning system, the cognitive inference and learning system executing on a hardware processor of an information processing system and interacting with the plurality of data sources, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a cognitive engine, the cognitive engine processing the streams of data from the plurality of data sources;

incorporating enriched data resulting from the performed data enriching into the cognitive graph as nodes within the cognitive graph;

defining a cognitive persona within the cognitive graph, the cognitive persona corresponding to an archetype user model, the defining comprising associating attributes with respective nodes of a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph, weighting of the links representing a relevance between attributes associated with respective nodes;

associating a user with the cognitive persona; and,

performing a cognitive computing operation based upon the cognitive persona associated with the user, the cognitive computing operation comprising at least one of performing a spatial navigation operation, a machine vision operation and a pattern recognition operation on at least some of the streams of data from the plurality of data sources.

2. The system of claim 1 , wherein:

the cognitive persona represents a set of attributes, each of the set of attributes corresponding to a node of the set of nodes; and,

an amount of weighting between nodes of the set of nodes corresponds to a degree of relevance between the persona and the attributes.

3. The system of claim 2 , wherein:

the set of attributes comprise at least one of demographic attributes, geographic attributes, psychographic attributes, and behavioristic attributes.

4. The system of claim 1 , wherein:

a link between a first node of the set of nodes and a second node of the set of nodes is represented by an attribute weight, the attribute weight indicating a degree of relevance between the attributes corresponding to the first node and the second node.

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

providing information to the user regarding the cognitive persona associated with the user;

receiving feedback from the user relating to the cognitive persona associated with the user; and,

revising weighting of the links among the set of nodes of the weighted cognitive graph based upon the feedback.

6. The system of claim 5 , wherein the instructions executable by the processor further comprise instructions for:

defining a second cognitive persona within the cognitive graph, the defining the second cognitive persona comprising associating attributes with respective nodes of a second set of nodes in the cognitive graph, links among the second set of nodes being weighted using a revised weighting resulting from the revising weighting of the lines.

7. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

receiving streams of data from a plurality of data sources;

processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph, the data enriching performing sentiment analysis, geotagging and entity detection operations on the streams of data from the plurality of data sources, the processing being performed by a cognitive inference and learning system, the cognitive inference and learning system executing on a hardware processor of an information processing system and interacting with the plurality of data sources, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a cognitive engine, the cognitive engine processing the streams of data from the plurality of data sources;

incorporating enriched data resulting from the performed data enriching into the cognitive graph as nodes within the cognitive graph;

defining a cognitive persona within the cognitive graph, the cognitive persona corresponding to an archetype user model, the defining comprising associating attributes with respective nodes of a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph, weighting of the links representing a relevance between attributes associated with respective nodes;

associating a user with the cognitive persona; and,

performing a cognitive computing operation based upon the cognitive persona associated with the user, the cognitive computing operation comprising at least one of performing a spatial navigation operation, a machine vision operation and a pattern recognition operation on at least some of the streams of data from the plurality of data sources.

8. The non-transitory, computer-readable storage medium of claim 7 , wherein:

the cognitive persona represents a set of attributes, each of the set of attributes corresponding to a node of the set of nodes; and,

an amount of weighting between nodes of the set of nodes corresponds to a degree of relevance between the persona and the attributes.

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

the set of attributes comprise at least one of demographic attributes, geographic attributes, psychographic attributes, and behavioristic attributes.

10. The non-transitory, computer-readable storage medium of claim 7 , wherein:

a link between a first node of the set of nodes and a second node of the set of nodes is represented by an attribute weight, the attribute weight indicating a degree of relevance between the attributes corresponding to the first node and the second node.

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

providing information to the user regarding the cognitive persona associated with the user;

receiving feedback from the user relating to the cognitive persona associated with the user; and,

revising weighting of the links among the set of nodes of the weighted cognitive graph based upon the feedback.

12. The non-transitory, computer-readable storage medium of claim 11 , wherein the instructions executable by the processor further comprise instructions for:

defining a second cognitive persona within the cognitive graph, the defining the second cognitive persona comprising associating attributes with respective nodes of a second set of nodes in the cognitive graph, links among the second set of nodes being weighted using a revised weighting resulting from the revising weighting of the links.

13. The non-transitory, computer-readable storage medium of claim 7 , wherein the computer executable instructions are deployable to a client system from a server system at a remote location.

14. The non-transitory, computer-readable storage medium of claim 7 , wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.