Towards Neural Theorem Proving at Scale [article]

Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel
2018 arXiv   pre-print
Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their computational complexity, which renders them unusable on real world datasets. We focus on the Neural Theorem Prover (NTP) model proposed by Rocktäschel and Riedel (2017), a continuous relaxation of the Prolog backward chaining algorithm where unification between terms is replaced by the similarity between their
more » ... g representations. For answering a given query, this model needs to consider all possible proof paths, and then aggregate results - this quickly becomes infeasible even for small Knowledge Bases (KBs). We observe that we can accurately approximate the inference process in this model by considering only proof paths associated with the highest proof scores. This enables inference and learning on previously impracticable KBs.
arXiv:1807.08204v1 fatcat:wh3gxfqnhvegbe5il4u4h6ldvm