We started using AWS Neptune (you can use it either with SPARQL or Gremlin) for a medical knowledge graph and while the AWS service is very good, the problem is the same as with any other NoSQL database:
Schemaless is ok if you just need to throw data and then analyze it in whatever way you find, but for two way data flow (ie: using written data in a user facing application), schemaless is a true headache and you end up keeping your schema in the application level anyway.
Besides that, the experience have been great, our hyphotesis of evolving our schema of concepts and relations between them freely have been proved successful.
Schemaless is ok if you just need to throw data and then analyze it in whatever way you find, but for two way data flow (ie: using written data in a user facing application), schemaless is a true headache and you end up keeping your schema in the application level anyway.
Besides that, the experience have been great, our hyphotesis of evolving our schema of concepts and relations between them freely have been proved successful.