End-to-End Differentiable Proving

NeurIPS 2017 Tim RocktäschelSebastian Riedel

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog... (read more)

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