Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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 ll as rules of predefined structure. Furthermore, they still allow us to incorporate domainknowledge 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.
['Tim Rocktäschel', 'Sebastian Riedel']
I am writing a research paper on a idea Learning Knowledge Base Inference with Neural Theorem Provers
[{"from": "human", "value": "I am writing a research paper on a idea Learning Knowledge Base Inference with Neural Theorem Provers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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 ll as rules of predefined structure. Furthermore, they still allow us to incorporate domainknowledge 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."}]