Universal graph output via insight agent accessing the universal graph
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; and, generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph, the generating the cognitive insight using an insight agent to access the collection of knowledge elements.
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 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, the processing being performed via 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 and the information processing system providing a cognitive computing function, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a cognitive engine, the cognitive engine processing the data from the plurality of data sources;
storing the knowledge elements within a cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, the cognitive graph comprising integrated machine learning functionality, the integrated machine learning functionality using extracted features of newly-observed data from user feedback received during a learning phase to improve accuracy of knowledge stored within the cognitive graph; and,
performing mapping operations on a query to generate query related knowledge elements based upon the user feedback, 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 an insight agent to the universal knowledge repository;
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; and,
generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph, the generating the cognitive insight using the insight agent to access the collection of knowledge elements.
2. The system of claim 1 , wherein the instructions executable by the processor further comprise instructions for:
accessing a plurality of query related knowledge elements within the cognitive graph via the insight agent;
processing the query related insights to identify a meaning of a query from a user;
accessing the plurality of answer related knowledge elements within the cognitive graph via the insight agent.
3. The system of claim 2 , wherein:
accessing the plurality of query related knowledge elements further comprises the insight agent traversing the plurality of query related knowledge elements within the cognitive graphs.
4. The system of claim 3 , wherein:
the query related knowledge elements are stored within the cognitive graph as nodes;
subsets of nodes are related via edges; and,
the traversing the plurality of query related knowledge elements comprises the insight agent accessing nodes of interest via parent nodes and child nodes based upon nodes related via the edges.
5. The system of claim 2 , wherein the instructions executable by the processor further comprise instructions for:
accessing the plurality of answer related knowledge elements further comprises the insight agent traversing the plurality of answer related knowledge elements within the cognitive graphs, the traversing the plurality of answer related knowledge elements being based upon the meaning of the query inferred by the insight agent.
6. The system of claim 5 , wherein:
the query related knowledge elements are stored within the cognitive graph as nodes;
subsets of nodes are related via edges; and,
the traversing the plurality of answer related knowledge elements comprises the insight agent accessing nodes of interest via parent nodes and child nodes based upon nodes related via the edges.
7. A non-transitory, computer-readable storage medium embodying computer program code, 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, the processing being performed via 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 and the information processing system providing a cognitive computing function, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a cognitive engine, the cognitive engine processing the data from the plurality of data sources;
storing the knowledge elements within a cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, the cognitive graph comprising integrated machine learning functionality, the integrated machine learning functionality using extracted features of newly-observed data from user feedback received during a learning phase to improve accuracy of knowledge stored within the cognitive graph;
performing mapping operations on a query to generate query related knowledge elements based upon the user feedback, 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 an insight agent to the universal knowledge repository;
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; and,
generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph, the generating the cognitive insight using the insight agent to access the collection of knowledge elements.
8. The non-transitory, computer-readable storage medium of claim 7 , wherein the instructions executable by the processor further comprise instructions for:
accessing a plurality of query related knowledge elements within the cognitive graph via the insight agent;
processing the query related insights to identify a meaning of a query from a user;
accessing the plurality of answer related knowledge elements within the cognitive graph via the insight agent.
9. The non-transitory, computer-readable storage medium of claim 8 , wherein:
accessing the plurality of query related knowledge elements further comprises the insight agent traversing the plurality of query related knowledge elements within the cognitive graphs.
10. The non-transitory, computer-readable storage medium of claim 9 , wherein:
the query related knowledge elements are stored within the cognitive graph as nodes;
subsets of nodes are related via edges; and,
the traversing the plurality of query related knowledge elements comprises the insight agent accessing nodes of interest via parent nodes and child nodes based upon nodes related via the edges.
11. The non-transitory, computer-readable storage medium of claim 8 , wherein the instructions executable by the processor further comprise instructions for:
accessing the plurality of answer related knowledge elements further comprises the insight agent traversing the plurality of answer related knowledge elements within the cognitive graphs, the traversing the plurality of answer related knowledge elements being based upon the meaning of the query inferred by the insight agent.
12. The non-transitory, computer-readable storage medium of claim 11 , wherein
the query related knowledge elements are stored within the cognitive graph as nodes;
subsets of nodes are related via edges; and,
the traversing the plurality of answer related knowledge elements comprises the insight agent accessing nodes of interest via parent nodes and child nodes based upon nodes related via the edges.
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.