I couldn't claim to know what is "correct" and what isn't! I've just worked on projects to organize large collections of interrelated datasets (for example, to

I couldn't claim to know what is "correct" and what isn't! I've just worked on projects to organize large collections of interrelated datasets (for example, to update correlations between concepts, to make search engines more effective, to identify related or dependent item relationships, etc) and for our project we used a Semantic Web stack. Browsing GitHub for "knowledge graph" or "knowledge management" seems to pop up some cool looking projects, but I think everyone is still trying to figure out what works for a particular use case rather than generally. I hope a real data scientist can reply with whatever the latest and greatest solutions are. Semantic Web tech for knowledge graphs are continuing to evolve, but also kind of old, and they're still mostly used for research projects. Part of that is probably because the terminology is unusual, and implementation leads you down a long rabbit hole of new and confusing concepts. So that's why I'm thinking that just sticking to a boring inefficient hierarchy might not be so bad...