Ingestion pipeline for universal cognitive 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 data source; determining whether the data comprises text; processing the data, the processing comprising performing a natural language processing operation on the data, the processing the data identifying a plurality of knowledge elements based upon the natural language processing operation; and, storing at least some of the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data.
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 data source;
determining whether the data comprises text;
processing the data, the processing comprising performing a natural language processing operation on the data, the processing the data identifying a plurality of knowledge elements based upon the natural language processing operation;
storing at least some of 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, the cognitive graph being implemented with an ontology, the ontology universally representing knowledge and comprising a representation of entities along with properties and relations of the entities according to a system of categories, the ontology storing a knowledge element within the cognitive graph based upon a set of categories of the knowledge element and a set of attributes of the knowledge element;
performing a parsing operation, the parsing operation generating a set of parse trees using a parse rule set, the parsing operation comprising a mapping operation, the mapping operation comprising mapping structural elements to resolve ambiguity, the mapping operation comprising mapping structural elements of the text around a verb of the text, 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; and,
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.
2. The system of claim 1 , wherein:
the processing comprises identifying the plurality of knowledge elements by performing the parsing operation on the text.
3. The system of claim 2 , wherein:
the processing comprises performing a machine learning operation on the plurality of knowledge elements, the machine learning operation resolving which knowledge elements within a parse tree from the plurality of parse trees provides a result representing a meaning of the text, the machine learning operation identifying knowledge elements of the plurality of parse trees representing ambiguous portions of the text.
4. The system of claim 3 , wherein:
the machine learning operation resolves the plurality of parse trees to a tree representing an interpretation of the ambiguous portions of the text.
5. The system of claim 2 , wherein:
the processing comprises performing the conceptualization operation on the plurality of knowledge elements, the conceptualization operation identifying a relationship of concepts identified from within the plurality of parse trees produced via the parsing operation and storing knowledge elements within the cognitive graph in a configuration representing the relationship of concepts.
6. The system of claim 5 , wherein:
the conceptualization operation identifies the relationship of the concepts using information stored within the cognitive graph.
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 data source;
determining whether the data comprises
text;
processing the data, the processing comprising performing a natural language processing operation on the data, the processing the data identifying a plurality of knowledge elements based upon the natural language processing operation;
storing at least some of 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, the cognitive graph being implemented with an ontology, the ontology universally representing knowledge and comprising a representation of entities along with properties and relations of the entities according to a system of categories, the ontology storing a knowledge element within the cognitive graph based upon a set of categories of the knowledge element and a set of attributes of the knowledge element;
performing a parsing operation, the parsing operation generating a set of parse trees using a parse rule set, the parsing operation comprising a mapping operation, the mapping operation comprising mapping structural elements to resolve ambiguity, the mapping operation comprising mapping structural elements of the text around a verb of the text, 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; and,
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.
8. The non-transitory, computer-readable storage medium of claim 7 , wherein:
the processing comprises identifying the plurality of knowledge elements by performing the parsing operation on the text.
9. The non-transitory, computer-readable storage medium of claim 8 , wherein:
the processing comprises performing a machine learning operation on the plurality of knowledge elements, the machine learning operation resolving which knowledge elements within the parse trees provide a result representing a meaning of the text, the machine learning operation identifying knowledge elements of the plurality of parse trees representing ambiguous portions of the text.
10. The non-transitory, computer-readable storage medium of claim 9 , wherein:
the machine learning operation resolves the plurality of parse trees to a tree representing an interpretation of the ambiguous portions of the text.
11. The non-transitory, computer-readable storage medium of claim 8 , wherein:
the processing comprises performing the conceptualization operation on the plurality of knowledge elements, the conceptualization operation identifying a relationship of concepts identified from within the plurality of parse trees produced via the parsing operation and storing knowledge elements within the cognitive graph in a configuration representing the relationship of concepts.
12. The non-transitory, computer-readable storage medium of claim 11 , wherein
the conceptualization operation identifies the relationship of the concepts using information stored within the cognitive graph.
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.