This is an implementation of the paper End-to-End Differentiable Proving. For a high-level introduction, see the NIPS oral, slides and poster.
Please note that this software is not maintained. It is highly-experimental research code, not well documented and we provide no warranty of any kind. Use at your own risk!
Data for the NTP is in nl
format - basically Prolog syntax:
ntp$ head data/countries/countries.nl
locatedIn(palau,micronesia).
locatedIn(palau,oceania).
locatedIn(maldives,southern_asia).
locatedIn(maldives,asia).
locatedIn(brunei,south-eastern_asia).
locatedIn(brunei,asia).
neighborOf(brunei,malaysia).
locatedIn(japan,eastern_asia).
locatedIn(japan,asia).
locatedIn(netherlands,western_europe).
*.nl
files represent facts and rules (example of a rule: isa(X,Y) :- isa(X,Z), isa(Z,Y)
)
*.nlt
files represent rule templates (example of a rule template: #1(X,Y) :- #2(X,Z), #3(Z,Y)
)
ntp$ cat data/ntp/simpsons.nlt
5 #1(X, Y) :- #2(X, Y).
5 #1(X, Y) :- #1(Y, X).
5 #1(X, Y) :-
#2(X, Z),
#2(Z, Y).
The main file for running NTP is ntp/experiments/learn.py
which takes the path to a configuration file as argument.
The core implementation of the NTP can be found here.
The base models (neural link predictors) are implemented here.
Imortant "modules" are unify, this one and this one. It should pretty much reflect the pseudocode in the paper.
The tricky part is the tiling of batched representations for batch proving - check out this.
However, this tiling needs to happen at various points in the code, e.g. here
Implementation of tiling (and multiplexing) here and here.
An important trick in NTP for proving in larger KBs and usin complex rules, is the Kmax heuristic, implemented here.
There is a symbolic prover implementation here
nosetests
@inproceedings{rocktaschel2017end,
author = {Tim Rockt{\"{a}}schel and
Sebastian Riedel},
title = {End-to-end Differentiable Proving},
booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference
on Neural Information Processing Systems 2017, 4-9 December 2017,
Long Beach, CA, {USA}},
pages = {3791--3803},
year = {2017},
url = {http://papers.nips.cc/paper/6969-end-to-end-differentiable-proving},
}