Lossless parsing when storing knowledge elements within a universal cognitive graph

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 lossless parsing operation on the data, the processing the data identifying a plurality of knowledge elements based upon the parsing operation, the lossless parsing operation generating a set of parse trees using a parse rule set, the lossless 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;

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; and,

storing the knowledge elements within the 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.

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

generating a set of parse options by performing the lossless parsing operation using the parse rule set, each of the parse options representing a respective parse tree within a parse forest.

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

providing a parse ranking rule set; and

ranking the set of parse options using the parse ranking set, the ranking providing a determination of highly ranked parse options.

4. The system of claim 3 , wherein the instructions executable by the processor further comprise instructions for:

performing conceptualization processes on the highly ranked parse options to provide the set of conceptualization ambiguity options.

5. The system of claim 4 , wherein the instructions executable by the processor further comprise instructions for:

applying a conceptualization rule set to the set of conceptualization ambiguity options; and,

ranking the set of conceptualization ambiguity options to provide highly ranked conceptualization options.

6. 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 lossless parsing operation on the data, the processing the data identifying a plurality of knowledge elements based upon the parsing operation, the lossless parsing operation generating a set of parse trees using a parse rule set, the lossless 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;

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; and,

storing the knowledge elements within the 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.

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

generating a set of parse options by performing the lossless parsing operation using the parse rule set, each of the parse options representing a respective parse tree within a parse forest.

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

providing a parse ranking rule set; and

ranking the parse options using the parse ranking set, the ranking providing a determination of highly ranked parse options.

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

performing conceptualization processes on the highly ranked parse options to provide the set of conceptualization ambiguity options.

10. The non-transitory, computer-readable storage medium of claim 9 , wherein the instructions executable by the processor further comprise instructions for:

applying a conceptualization rule set to the set of conceptualization ambiguity options; and,

ranking the set of conceptualization ambiguity options to provide highly ranked conceptualization options.

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.