Scalable Neural Methods for Reasoning With a Symbolic Knowledge BaseDownload PDF

25 Sept 2019, 19:17 (edited 10 Feb 2022)ICLR 2020 Conference Blind SubmissionReaders: Everyone
  • Original Pdf: pdf
  • Data: [MetaQA](https://paperswithcode.com/dataset/metaqa), [NELL-995](https://paperswithcode.com/dataset/nell-995), [WebQuestions](https://paperswithcode.com/dataset/webquestions), [WebQuestionsSP](https://paperswithcode.com/dataset/webquestionssp)
  • TL;DR: A scalable differentiable neural module that implements reasoning on symbolic KBs.
  • Abstract: We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically large KBs. The sparse-matrix reified KB can be distributed across multiple GPUs, can scale to tens of millions of entities and facts, and is orders of magnitude faster than naive sparse-matrix implementations. The reified KB enables very simple end-to-end architectures to obtain competitive performance on several benchmarks representing two families of tasks: KB completion, and learning semantic parsers from denotations.
  • Keywords: question-answering, knowledge base completion, neuro-symbolic reasoning, multihop reasoning
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