Examples relate to systems for authoring and executing predictive models. A computer system includes a model development context analyzer configured to store a set of derived modeling knowledge generated at least in part from a plurality of modeling operations performed using at least a first predictive model authoring tool. The system is configured to, receive a modeling context indicating at least a modeling operation being performed, determine, from the modeling context, at least one element of an ontology, the ontology defining at least one attribute of a plurality of modeling operations, query the set of derived modeling knowledge using the at least one element of the ontology to identify at least one record of the set of derived modeling knowledge associated with the at least one element of the ontology, identify at least one suggested model parameter associated with the modeling context, and provide the at least one suggested model parameter.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system configured to programmatically identify gaps in a predictive model knowledge graph, the computer system comprising: a model development context analyzer configured to: generate a knowledge graph comprising a plurality of elements indicating a plurality of correlations between model parameters used in modeling operations performed using a predictive model authoring tool; analyze the knowledge graph to identify at least one of the plurality of elements that is not associated with a data attribute; determine a user associated with the identified at least one of the plurality of elements; generate a user interface to present to the user, the user interface comprising at least one interface control for providing a response to a query derived from the identified at least one of the plurality of elements; receive the response to the query via the at least one interface control; and update the knowledge graph based on the response to the query. 2. The computer system of claim 1, wherein the computer system is further configured to analyze the knowledge graph to identify at least one of the plurality of elements that is not associated with a data attribute by at least: querying the knowledge graph to determine a plurality of records associated with a particular element of an ontology and an attribute value for the particular element; determining at least one common characteristic of the plurality of records; determining that at least one element of the ontology lacks the at least one common characteristic; and identifying the at least one element of the ontology as not being associated with the data attribute. 3. The computer system of claim 2, further configured to: segment the plurality of records into at least a first set of records and a second set of records, each of the plurality of records associated with a particular asset type and each of the first set of records and the second set of records associated with a respective asset sub-type; determine a common characteristic among the first set of records; determine that the second set of records lacks the common characteristic; and identify the second set of records as not being associated with the data attribute in response to determining that the second set of records lacks the common characteristic. 4. The computer system of claim 1, further configured to analyze the knowledge graph to identify the at least one of the plurality of elements not associated with the data attribute in response to a new record being added to the knowledge graph. 5. The computer system of claim 1, further configured to analyze each element of the knowledge graph to identify the at least one of the plurality of elements not associated with the data attribute by iteratively analyzing through the elements of the knowledge graph. 6. The computer system of claim 1, wherein the knowledge graph is structured according to an ontology, and wherein analyzing the knowledge graph comprises performing queries using the ontology and at least one attribute value for an element of the ontology. 7. The computer system of claim 6, wherein the ontology is hierarchical, such that least one element of the ontology has at least one sub-element. 8. The computer system of claim 1, further configured to determine the at least one user based at least in part on an organization of the user and an organization associated with the identified at least one of the plurality of elements. 9. The computer system of claim 1, further configured to: generate a validation interface for validation of the response to the query; receive a validation indication via the validation interface; and update the knowledge graph only in response to receiving the validation indication. 10. A method for generating an interface for programmatically identifying and addressing gaps in a predictive model knowledge graph, the method comprising: generating a knowledge graph comprising a plurality of elements indicating a plurality of correlations between model parameters used in modeling operations performed using a predictive model authoring tool; analyzing the knowledge graph to identify at least one of the plurality of elements that is not associated with a data attribute; determining a user associated with the identified at least one of the plurality of elements; generating a user interface to present to the user, the user interface comprising at least one interface control for providing a response to a query derived from the identified at least one of the plurality of elements; receiving the response to the query via the at least one interface control; and updating the knowledge graph based on the response to the query. 11. The method of claim 10, further comprising analyzing the knowledge graph to identify at least one of the plurality of elements that is not associated with a data attribute by at least: querying the knowledge graph to determine a plurality of records associated with a particular element of an ontology and an attribute value for the particular element; determining at least one common characteristic of the plurality of records; determining that at least one element of the ontology lacks the at least one common characteristic; and identifying the at least one element of the ontology as not being associated with the data attribute. 12. The method of claim 11, further comprising: segmenting the plurality of records into at least a first set of records and a second set of records, each of the plurality of records associated with a particular asset type and each of the first set of records and the second set of records associated with a respective asset sub-type; determining a common characteristic among the first set of records; determining that the second set of records lacks the common characteristic; and identifying the second set of records as not being associated with the data attribute in response to determining that the second set of records lacks the common characteristic. 13. The method of claim 10, further comprising analyzing the knowledge graph to identify the at least one of the plurality of elements not associated with the data attribute in response to a new record being added to the knowledge graph. 14. The method of claim 10, further comprising analyzing each element of the knowledge graph to identify the at least one of the plurality of elements not associated with the data attribute by iteratively analyzing through the elements of the knowledge graph. 15. The method of claim 10, wherein the knowledge graph is structured according to an ontology, and wherein analyzing the knowledge graph comprises performing queries using the ontology and at least one attribute value for an element of the ontology. 16. The method of claim 15, wherein the ontology is hierarchical, such that least one element of the ontology has at least one sub-element. 17. The method of claim 10, further comprising determining the at least one user based at least in part on an organization of the user and an organization associated with the identified at least one of the plurality of elements. 18. The method of claim 10, further comprising: generating a validation interface for validation of the response to the query; receiving a validation indication via the validation interface; and updating the knowledge graph only in response to receiving the validation indication. 19. A non-transitory computer readable storage medium comprising instructions that, when executed by a computer processor, cause the computer processor to implement a method for generating an interface for programmatically identifying and addressing gaps in a predictive model knowledge graph, the program instructions comprising instructions for: generating a knowledge graph comprising a plurality of elements indicating a plurality of correlations between model parameters used in modeling operations performed using a predictive model authoring tool; analyzing the knowledge graph to identify at least one of the plurality of elements that is not associated with a data attribute; determining a user associated with the identified at least one of the plurality of elements; generating a user interface to present to the user, the user interface comprising at least one interface control for providing a response to a query derived from the identified at least one of the plurality of elements; receiving the response to the query via the at least one interface control; and updating the knowledge graph based on the response to the query. 20. The non-transitory computer readable storage medium of claim 19, further comprising program instructions comprising instructions for analyzing the knowledge graph to identify at least one of the plurality of elements that is not associated with a data attribute by at least: querying the knowledge graph to determine a plurality of records associated with a particular element of an ontology and an attribute value for the particular element; determining at least one common characteristic of the plurality of records; determining that at least one element of the ontology lacks the at least one common characteristic; and identifying the at least one element of the ontology as not being associated with the data attribute.