Conceptarium

I feel like ultimately you want both. Explicit links are a useful navigation affordance with nice properties that spatial embeddings won't give you (e.g. you can explicitly establish a link between things that are not similar according to the embedding space).

More important than that though, explicit links let you train the embedding model to understand the dataset the way the user does. All of these embedding models are trained on graphs (word or sentence cooccurence graphs, parent/child comment graphs on social media, etc.). The graph structure in something like Roam can provide training data for updating and adapting the embedding space to the specific knowledge context in which it's used.

Conversely, if you have an embedding representation of your knowledge base, you can use that to suggest explicit links. The embedding space is the dense dual to the sparse graph of explicit links in something like Roam. It's a fully connected weighted graph rather than a sparse unweighted graph.

Maybe this system is meant to only focus on the spatial embedding representation. That makes a lot of sense. A fully-fledged version of this vision though IMO should include a bridge between these two dual representations.

A big challenge I have is when I add a new tag, I'll want to apply it to relevant content I already have. But this takes a long time and until it happens the relevant data can't be retrieved by the tag.

I am curious how the semantic hash is implemented in a conceptarium. If I could focus on adding content and it can drive relationships automatically, that is awesome.

I plan on releasing something soon, this is super interesting!

Also, I appreciate the heavy use of Greg Egan novels at the end!