[xabush]

How does Scallop scale on large knowledge bases (KBs) for doing probabilistic reasoning? I'm currently working on large KB with ~ 12M facts and trying to do probabilistic inference on it. So far I've been using [cplint](https://friguzzi.github.io/cplint/_build/html/index.html) which is based on SWI-Prolog. It works fine for toy examples, however, it doesn't finish running for the large KB - even after waiting for it for more than a week. Does know any Probabilistic Logic Programming (PLP) libraries that are fast and scale to large KBs? Preferably in Prolog ecosystem, but not a hard requirement.

[nextos]

ProbLog? https://dtai.cs.kuleuven.be/problog

[riku_iki]

I am surprised you have problems with 12M facts and can't process them in a week, looks like bug in software you are using.

[xabush]

Thanks for the comment. Have you run cplint on a kb of the similar size before and gotten it to finish in reasonable time?

[riku_iki]

I never used cplint, but I use other software (including I built myself) to process KBs with many billions of facts.

[xabush]

Is the software open-source? If so, can you share a link to it?