Providing a semantic encoding and language neural network

A system for providing semantic encoding and language generation in a computing environment, comprising:

• one or more computers with executable instructions that when executed cause the system to:
• automatically parse unstructured data into one or more knowledge graphs based on the unstructured data and a list of candidate relations using a first machine learning model;
• encode, using the first machine learning model, the unstructured data into a distribution of a plurality of triples based on the one or more knowledge graphs, wherein the encoding further comprises predicted probabilities of relations between entities in the unstructured data;
• sample, using a second machine learning model, a set of the plurality of triples from the unstructured data of the one or more knowledge graphs;
• generate text data from the set of the plurality of triples using the second machine learning model;
• compute a penalty score for the set of the plurality of triples based on a degree of difference between the unstructured data and the generated text data;
• and
• adjust at least one predicted probability from the first machine learning model based on the determined penalty score.

A computer program product for providing semantic encoding and language generation in a computing environment, the computer program product comprising:

• one or more tangible computer readable storage media, and program instructions collectively stored on the one or more tangible computer readable storage media, the program instruction comprising:
• automatically parse unstructured data into one or more knowledge graphs based on the unstructured data and a list of candidate relations using a first machine learning model;
• encode, using the first machine learning model, the unstructured data into a distribution of a plurality of triples based on the one or more knowledge graphs, wherein the encoding further comprises predicted probabilities of relations between entities in the unstructured data;
• sample, using a second machine learning model, a set of the plurality of triples from the unstructured data of the one or more knowledge graphs;
• generate text data from the set of the plurality of triples using the second machine learning model;
• compute a penalty score for the set of the plurality of triples based on a degree of difference between the unstructured data and the generated text data;
• and
• adjust at least one predicted probability from the first machine learning model based on the determined penalty score.