Learning Knowledge Base Inference with Neural Theorem Provers

摘要

In this paper we present a proof-of-concept implementation of Neural Theorem Provers (NTPs), end-to-end differentiable counterparts of discrete theorem provers that perform first-order inference on vector representations of symbols using function-free, possibly parameterized, rules. As such, NTPs follow a long tradition of neural-symbolic approaches to automated knowledge base inference, but differ in that they are differentiable with respect to representations of symbols in a knowledge base and can thus learn representations of predicates, constants, as well as rules of predefined structure. Furthermore, they still allow us to incorporate domain-knowledge provided as rules. The NTP presented here is realized via a differentiable version of the backward chaining algorithm. It operates on substitution representations and is able to learn complex logical dependencies from training facts of small knowledge bases.