[DL-Learner discussion] tips on large knowledge bases

[Diogo FC P. <djo...@gm...>]

Hello

First, congratulations on the release of this exciting project!

I have a knowledge base about cancer patients with 6.240.880 triples,
loaded on a open source virtuoso server. Before loading, we materialized
inferences using pellet - it took almost 3 days in a 30GB RAM server.

I have a configuration dl-learner file (based on the Actors example) which
queries the SPARQL endpoint. But as I set the recursion level to 4,
dl-learner end up taking too much memory and throws and exception.

I have two questions:

1) Is it possible to configure what would be queried by dl-learner? There
are data and object properties which could be removed from the analysis. Is
there a way to filter it?

2) Can I turn off the built-in inference provided by dl-learner?
(considering that everything is materialized in my KB).

Thanks!

--
diogo patrão