[strangattractor]

I had similar discussions with people that wanted to encode published research into ontologies. I would ask researchers what they think - the answer was always great idea. I would then follow up with - How would you use it? No response. I finally concluded that it would never happen.

1. No one wanted it enough to pay for it to happen.

2. There is always a turn over of ideas coming and going which can never be sufficiently updated to keep it useful. Again no one would pay anyway.

Tools like LLMs seem to be fill the role now. I would like to see a Prolog integrated with LLMs is someway (lack of imagination fails me how that would happen).

[rscho]

A theorem prover for the medical literature:

https://github.com/webyrd/mediKanren

http://minikanren.org/workshop/2020/minikanren-2020-paper7.p...

Not prolog though. But gives an idea about the goals behind the classification of science papers.

[ta988]

How would you use it? For searches.

If I want to find something in the brain but not in bone structures. If I want to find something in a kind of cell but that have a nucleus.

They are also extremely useful for automated annotation. Your automated system may annotate with a upper term because it doesn't have enough information to be more precise. That's already a big help for a human to come and put a more precise term.

We are at a convergence of technologies, with ontologies, graphs, llms and logic programming. A lot of people were too early on this and discouraged from pursuing further by people that couldn't grasp why it was so important.