idk if it’s precisely the same but o3 recently offered to create one for me in, was it markdown?, recently. suggesting it was something it was willing to maintain for me.

I feel like you can do the same using a single markdown file and an LLM (e.g. Claude Code).

I do it that way and then I hooked it up with the Telegram API. I’m able to ask things like “What’s my passport number?” and it just works.

Combine it with git and you have a Datomic-esque way of seeing facts getting added and retracted simply by traversing the commits.

I arrived to the solution after trying more complex triplets-based approach and seeing that plain text-files + HTTP calls work as good and are human (and AI) friendly.

The main disadvantage is having unstructured data, but for content that fits inside the LLM context window, it doesn’t matter practically speaking. And even then, when context starts being the limiting factor, you can start segmenting by categories or start using embeddings.

People probably don't discuss the problems enough about an open world knowledge graph. Essentially the same class of problems as spam filters. Using an open language model to produce a graph doesn't create a closed world graph by definition. This confusion as well as just general avoidance of measuring actual productivity outcomes seems like an insurmountable problem in knowledge world now and I feel language itself is failing at times to educate on this issues.

Now imagine it with theorems as entities and lean proofs as relationships

Why not merely upload all relevant documents into Gemini? Split the knowledge into smaller knowledge domains and have agents ( backed by Gemini) for each domain?

building knowledge graphs (GrahRAGs) are obsolete from a acamedic and technical point of view. LLMs are getting better with built in graph networks capable algorithms like SONAR and knowledge embeddings. like someone said - just use Notebook LM instead. But, they are useful in corporate setup when the infrastructure,teams and skills are lagging by years.

I feel like I should understand the purpose of knowledge graphs, but I just... don't.

Like the example "CocoIndex supports Incremental Processing" becomes the subject/predicate/object triple (CocoIndex, supports, Incremental Processing)... so what? Are you going to look up "Incremental Processing" and get a list of related entities? That's not a term that is well enough defined to be meaningful across a variety of subjects. I can incrementally process my sandwich by taking small bites.

I guess you could actually expand "Incremental Processing" to some full definition. But then it's not really a knowledge graph because the only entity ever associated with that new definition will be CocoIndex, and you are back to a single sentence that contains the information, you've just pretended it's structured. ("Supports" hardly a well-defined term either!)

I can _kind of_ see how knowledge graphs can be used for limited relationships. If you want to map companies to board members, and board members to family members, etc. Very clearly and formally defined entities (like a person or company), with clearly defined relationships (board member, brother, etc). I still don't know how _useful_ the result is, but at least I can understand the validity of the model. But for everything else... am I missing something?