Apparatuses, methods, and computer program products for providing predictive inferences related to a graph representation of data via an application programming interface

1 . An apparatus comprising one or more processors and one or more storage devices storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to:

receive an application programming interface (API) object via a communication channel, wherein the API object defines a feature dataset associated with an API request for an application framework;

determine one or more graph feature sets that comprises one or more features associated with graph data that graphically represents relationships between component objects that respectively correspond to an application component of the application framework;

apply a graph neural network model to the one or more graph feature sets to generate one or more graph embedding structures for utilization by a machine learning model;

apply the machine learning model to (i) the API object and (ii) the one or more graph embedding structures for a portion of the graph data associated with a respective entity identifier to generate one or more predictive inferences related to the API object, wherein the one or more graph embedding structures respectively represent an encoded version of one or more of the relationships for the respective entity identifier; and

initiate a response action for the API request based at least in part on the one or more predictive inferences.

2 . The apparatus of claim 1 , wherein the API object is a service message object that defines the feature dataset for a service request for the application framework, wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

apply the machine learning model to the service message object and the one or more graph embedding structures to generate the one or more predictive inferences, wherein the response action is initiated for the service request based at least in part on the one or more predictive inferences.

3 . The apparatus of claim 1 , wherein the API object is a recommendation system object that defines the feature dataset for a relevancy request related to the component objects of the application framework, wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

apply the machine learning model to the recommendation system object and the one or more graph embedding structures to generate the one or more predictive inferences, wherein the response action is initiated for the relevancy request based at least in part on the one or more predictive inferences.

4 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

compare the API object to the one or more graph embedding structures using a scoring function associated with the machine learning model to generate the one or more predictive inferences.

5 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

compare the API object to the one or more graph embedding structures using a clustering technique associated with the machine learning model to generate the one or more predictive inferences.

6 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

determine one or more patterns related to the one or more graph embedding structures based on the feature dataset to generate the one or more predictive inferences; and

predict one or more future relationships related to the one or more graph embedding structures based on the feature dataset to generate the one or more predictive inferences.

7 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

initiate one or more actions via an application layer of the application framework based at least in part on the one or more predictive inferences.

8 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

initiate one or more actions via a virtual agent system based at least in part on the one or more predictive inferences.

9 . The apparatus of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:

transmit, via the communication channel, a response message object for a client device based at least in part on the one or more predictive inferences.

10 . The apparatus of claim 1 , wherein the communication channel corresponds to an email communication channel, a network portal interface communication channel, a user interface widget communication channel, a chat communication channel, or an API communication channel.

11 . A computer-implemented method, comprising:

receiving an application programming interface (API) object via a communication channel, wherein the API object defines a feature dataset associated with an API request for an application framework;

determining one or more graph feature sets that comprises one or more features associated with graph data that graphically represents relationships between component objects that respectively correspond to an application component of the application framework;

applying a graph neural network model to the one or more graph feature sets to generate one or more graph embedding structures for utilization by a machine learning model;

applying the machine learning model to (i) the API object and (ii) the one or more graph embedding structures for a portion of the graph data associated with a respective entity identifier to generate one or more predictive inferences related to the API object, wherein the one or more graph embedding structures respectively represent an encoded version of one or more of the relationships for the respective entity identifier; and

initiating a response action for the API request based at least in part on the one or more predictive inferences.

12 . The computer-implemented method of claim 11 , wherein applying the machine learning model comprises:

comparing the API object to the one or more graph embedding structures using a scoring function associated with the machine learning model to generate the one or more predictive inferences.

13 . The computer-implemented method of claim 11 , wherein applying the machine learning model comprises:

comparing the API object to the one or more graph embedding structures using a clustering technique associated with the machine learning model to generate the one or more predictive inferences.

14 . The computer-implemented method of claim 11 , wherein applying the machine learning model comprises:

determining one or more patterns related to the one or more graph embedding structures based on the feature dataset to generate the one or more predictive inferences; and

predicting one or more future relationships related to the one or more graph embedding structures based on the feature dataset to generate the one or more predictive inferences.

15 . The computer-implemented method of claim 11 , wherein initiating the resolution action comprises:

initiate one or more actions via an application layer of the application framework based at least in part on the one or more predictive inferences.

16 . A computer program product, stored on a computer-readable storage medium, comprising instructions that when executed by one or more computers cause the one or more computers to:

receive an application programming interface (API) object via a communication channel, wherein the API object defines a feature dataset associated with an API request for an application framework;

determine one or more graph feature sets that comprises one or more features associated with graph data that graphically represents relationships between component objects that respectively correspond to an application component of the application framework;

apply a graph neural network model to the one or more graph feature sets to generate one or more graph embedding structures for utilization by a machine learning model;

apply the machine learning model to (i) the API object and (ii) the one or more graph embedding structures for a portion of the graph data associated with a respective entity identifier to generate one or more predictive inferences related to the API object, wherein the one or more graph embedding structures respectively represent an encoded version of one or more of the relationships for the respective entity identifier; and

initiate a response action for the API request based at least in part on the one or more predictive inferences.

17 . The computer program product of claim 16 , wherein the API object is a service message object that defines the feature dataset for a service request for the application framework, and wherein the instructions further cause the one or more computers to:

apply the machine learning model to the service message object and the one or more graph embedding structures to generate the one or more predictive inferences, wherein the response action is initiated for the service request based at least in part on the one or more predictive inferences.

18 . The computer program product of claim 16 , wherein the API object is a recommendation system object that defines the feature dataset for a relevancy request related to the component objects of the application framework, and wherein the instructions further cause the one or more computers to:

apply the machine learning model to the recommendation system object and the one or more graph embedding structures to generate the one or more predictive inferences, wherein the response action is initiated for the relevancy request based at least in part on the one or more predictive inferences.

19 . The computer program product of claim 16 , wherein the instructions further cause the one or more computers to:

compare the API object to the one or more graph embedding structures using a scoring function associated with the machine learning model to generate the one or more predictive inferences.

20 . The computer program product of claim 16 , wherein the instructions further cause the one or more computers to:

compare the API object to the one or more graph embedding structures using a clustering technique associated with the machine learning model to generate the one or more predictive inferences.