I am in an enterprise which experiments with ontologies and knowledge graphs since its hype heydays ca. 15 years ago. It never took off, good ol’ relational databases and its modern day implementations in the cloud still rule the data landscape. Many smart consultants tried their best and we spend many millions over time into it. I would love to read more success stories, but they seem to be rare beyond academic exercises.

I mean, doesn't this exact statement also apply to the adoption of AI and all the winters that have come before this wave?

Also as someone doing my own implementation at the moment, I expect that there is a big relational database backing the MELT store, just the graph aspect is functionally a big RAG interface on top of it acting as a search cache, not itself the data store.

95% of sparql, cypher, and gremlin can be lowered to sql anyway. I rolled my own sql lowering system for duckdb over iceberg which lets me do everything in that blog post but with no bespoke data layer.