A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device, comprising: one or more processors to: receive issue resolution information associated with a cognitive model, the issue resolution information including an item of issue resolution information that describes an issue and a resolution corresponding to the issue; assign the item of issue resolution information to a particular domain hierarchy, the assigning of the item of issue resolution information to the particular domain hierarchy being associated with a first user; generate a set of questions and an answer corresponding to the item of issue resolution information, the generating of the set of questions and the answer being associated with a second user that is different from the first user; approve the set of questions and the answer corresponding to the item of issue resolution information, the approving of the set of questions and the answer being associated with a third user that is different from the first user and the second user; generate, based on approving the set of questions and the answer, a set of question/answer (QA) pairs for the set of questions and the answer, each QA pair, of the set of QA pairs, being associated with a question, of the set of questions, and the answer; create a data corpus including the set of QA pairs; and provide the data corpus to cause the cognitive model to be trained based on a portion of the data corpus. 2. The device of claim 1, where the one or more processors are further to: analyze domain hierarchy information, associated with a plurality of domain hierarchies, and the item of issue resolution information; automatically determine, based on analyzing the domain hierarchy information and the item of issue resolution information, that the item of issue resolution is to be assigned to the particular domain hierarchy, the particular domain hierarchy being one of the plurality of domain hierarchies; and where the one or more processors, when assigning the item of issue resolution information to the particular domain hierarchy, are to: assign the item of issue resolution information to the particular domain hierarchy based on automatically determining that the item of issue resolution is to be assigned to the particular domain hierarchy. 3. The device of claim 1, where the one or more processors are further to: analyze, using a paraphrasing technique, the item of issue resolution information; and where the one or more processors, when generating the set of questions and the answer corresponding to the item of issue resolution information, are to: automatically generate the set of questions and the answer based on analyzing the item of issue resolution information using the paraphrasing technique. 4. The device of claim 1, where the one or more processors are further to: provide, based on assigning the item of issue resolution information to the particular domain hierarchy, a notification to a user device associated with the second user, the notification being associated with notifying the second user that the item of issue resolution information has been assigned to the particular domain hierarchy. 5. The device of claim 1, where the one or more processors are further to: receive user input associated with approving the set of questions and the answer, the user input being provided by the third user; and where the one or more processors, when approving the set of questions and the answer corresponding to the item of issue resolution information, are to: approve the set of questions and the answer based on the user input. 6. The device of claim 1, where the one or more processors, after causing the cognitive model to be trained based on providing the data corpus, are further to: determine a validation metric based on an output of the cognitive model; determine whether the validation metric satisfies a threshold corresponding to the validation metric; and selectively: validate the training of the cognitive model when the validation metric satisfies the threshold, or cause the cognitive model to be retrained when the validation metric does not satisfy the threshold. 7. The device of claim 1, where the one or more processors are further to: receive domain hierarchy information that includes information associated with a plurality of domain hierarchies; and where the one or more processors, when assigning the item of issue resolution information to the particular domain hierarchy, are to: assign the item of issue resolution information to the particular domain hierarchy based on the domain hierarchy information, the particular domain hierarchy being one of the plurality of domain hierarchies. 8. A method, comprising: receiving, by a device, issue resolution information associated with a cognitive model, the issue resolution information including an item of issue resolution information that describes an issue and a resolution to the issue; assigning, by the device, the item of issue resolution information to a domain hierarchy of a plurality of domain hierarchies, the assigning of the item of issue resolution information to the domain hierarchy being associated with a first user; determining, by the device, one or more questions corresponding to the item of issue resolution information and an answer associated with the one or more questions, the determining of the one or more questions and the answer being associated with a second user that is different from the first user; approving, by the device, the one or more questions and the answer, the approving of the one or more questions and the answer being associated with a third user that is different from the first user and the second user; generating, by the device and based on approving the one or more questions and the answer, one or more question/answer (QA) pairs, each QA pair, of the one or more QA pairs, being associated with a question, of the one or more questions, and the answer; creating, by the device, a data corpus including the one or more QA pairs; and causing, by the device, the cognitive model to be trained based on a portion of the data corpus. 9. The method of claim 8, further comprising: receiving user input associated with assigning the item of issue resolution information to the domain hierarchy, the user input being provided by the first user; and where assigning the item of issue resolution information to the domain hierarchy comprises: assigning the item of issue resolution information to the domain hierarchy based on the user input provided by the first user. 10. The method of claim 8, further comprising: receiving user input associated with the one or more questions and the answer, the user input being provided by the second user; and where determining the one or more questions and the answer comprises: determining the one or more questions and the answer based on the user input provided by the second user. 11. The method of claim 8, further comprising: providing, based on assigning the item of issue resolution information to the domain hierarchy, a notification to a user device associated with the second user, the notification being associated with notifying the second user that the item of issue resolution information has been assigned to the domain hierarchy. 12. The method of claim 8, further comprising: receiving user input associated with approving the one or more questions and the answer, the user input being provided by the third user; and where approving the one or more questions and the answer comprises: approving the one or more questions and the answer based on the user input provided by the third user. 13. The method of claim 8, where, after causing the cognitive model to be trained based on the portion of the data corpus, the method further comprises: determining a performance metric based on an output of the cognitive model associated with another portion of the data corpus; and determining that the performance metric does not satisfy a threshold corresponding to the performance metric; and causing the cognitive model to be retrained based on determining that the performance metric does not satisfy the threshold corresponding to the performance metric. 14. The method of claim 13, where causing the cognitive model to be retrained comprises: causing the cognitive model to be retrained based on at least the other portion of the data corpus. 15. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: receive issue resolution information associated with a cognitive model, the issue resolution information including an item of issue resolution information that describes an issue and a resolution corresponding to the issue; assign the item of issue resolution information to a particular domain hierarchy; generate, based on assigning the item of issue resolution information to the particular domain hierarchy, a question and an answer corresponding to the item of issue resolution information; approve, based on generating the question and the answer, the question and the answer corresponding to the item of issue resolution information; generate, based on approving the question and the answer, a question/answer (QA) pair corresponding to the question and the answer; create a data corpus including the QA pair; and provide the data corpus, after creating the data corpus, to cause the cognitive model to be trained based on a portion of the data corpus. 16. The non-transitory computer-readable medium of claim 15, where: the assigning of the item of issue resolution information to the particular domain hierarchy is associated with a first user, the generating of the question and the answer is associated with a second user that is different from the first user, and the approving of the question and the answer is associated with a third user that is different from the first user and the second user. 17. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: analyze domain hierarchy information, associated with a plurality of domain hierarchies, and the item of issue resolution information; automatically determine, based on analyzing the domain hierarchy information and the item of issue resolution information that the item of issue resolution is to be assigned to the particular domain hierarchy, the particular domain hierarchy being one of the plurality of domain hierarchies; and where the one or more instructions, that cause the one or more processors to assign the item of issue resolution information to the particular domain hierarchy, cause the one or more processors to: assign the item of issue resolution to the particular domain hierarchy based on automatically determining that the item of issue resolution is to be assigned to the particular domain hierarchy. 18. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: receive user input associated with assigning the item of issue resolution information to the particular domain hierarchy; and where the one or more instructions, that cause the one or more processors to assign the item of issue resolution information to the particular domain hierarchy, cause the one or more processors to: assign the item of issue resolution information to the particular domain hierarchy based on the user input provided by a user associated with the particular domain hierarchy. 19. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: analyze, using a paraphrasing technique, the item of issue resolution information; and where the one or more instructions, that cause the one or more processors to generate the question and the answer corresponding to the item of issue resolution information, cause the one or more processors to: automatically generate the question and the answer based on analyzing the item of issue resolution information using the paraphrasing technique. 20. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: receive user input associated with the question and the answer, the user input being provided by a user associated with the particular domain hierarchy; and where the one or more instructions, that cause the one or more processors to generate the question and the answer corresponding to the item of issue resolution information, cause the one or more processors to: generate the question and the answer based on the user input.