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Hi, I'm trying to reproduce your evaluation for dILP in supervised tasks. But I'm stucking in designing the rules for task 1-outdegree for dILP.

• For 1-outdegree, the rules can be:

But how should we express it in dILP? which auxiliary predicates should we use to express the meaning: for all nodes `c`, which is unequal to `b`, and has no edge with node `a`?

I'll appreciate it if you can give me some suggestions!

Hi, I am trying to apply the NLM to other graph datasets, but I am a little confused on how to do it given the structure of the training script.

For instance, could you please shed some light on how to adapt the running scripts for other datasets like the Countries dataset*, that are bigger than the cases you presented in the paper and are also fixed (not randomly generated during each epoch)?

Thank you in advance for your time :)

• Guillaume Bouchard, Sameer Singh, and Theo Trouillon. On approximate reasoning capabilities of low-rank vector spaces.AAAI Spring Symposium, 2015

Hi,

I noticed your paper showed the results by DILP, but I couldn't find the corresponding code about the DILP implementation. Could you please share the code with me? Many thanks!