  Semi-structured data formats such as JSON have proved to be useful data
models for applications that require flexibility in the format of data stored.
However, JSON data often come without the schemas that are typically available
with relational data. This has resulted in a number of tools for discovering
schemas from a collection of data. Although such tools can be useful, existing
approaches focus on the syntax of documents and ignore semantic information.
  In this work, we explore the automatic addition of meaningful semantic
information to discovered schemas similar to information that is added by human
schema authors. We leverage large language models and a corpus of manually
authored JSON Schema documents to generate natural language descriptions of
schema elements, meaningful names for reusable definitions, and identify which
discovered properties are most useful and which can be considered "noise". Our
approach performs well on existing metrics for text generation that have been
previously shown to correlate well with human judgement.
