I agree that it would be limited by current data. But I think this would be highly useful in two specific scenarios:
- someone new to PKM systems. It would give you hints (based on what other users in the past have done before, of course), on what other notes you may want to link to the current one (it’s up to the user to add the appropriate context, of course)
- Someone with a large graph that may be forgetting about some old note. Imagine there is some older note, in a completely different place of your vault, but that is somewhat complimentary the your current note. This would be useful to know!
About the lack of novelty, if we can get enough data (from multiple users, that is), the model would learn to link notes using diverse methodologies, so it could give you suggestions that you would not think yourself, but the “obsidian hive mind” would.
About the leap in machine understanding, I don’t feel it’s too far-fetched. There are plenty of works using pre-trained language models (I.e BERT, T5, etc) that deal pretty well with the link prediction problem (https://www.aclweb.org/anthology/2020.coling-main.153.pdf)