Semantic Data Modeling, Graph Query, and SQL, Together at Last?

DRANK

AbstractSemantic data models express high-level business concepts and metrics, capturing the business logic needed to query a database correctly. Most data modeling solutions are built as layers above SQL query engines, with bespoke query languages or APIs. The layered approach means that semantic models can’t be used directly in SQL queries. This paper focuses on an open problem in this space – can we define semantic models in SQL, and make them naturally queryable in SQL?In parallel, graph query is becoming increasingly popular, including in SQL. SQL/PGQ extends SQL with an embedded subset of the GQL graph query language, adding property graph views and making graph traversal queries easy.We explore a surprising connection: semantic data models are graphs, and defining graphs is a data modeling problem. In both domains, users start by defining a graph model, and need query language support to easily traverse edges in the graph, which means doing joins in the underlying data.We pro…