Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base

• URL: http://arxiv.org/abs/2002.06115v1
• Date: Fri, 14 Feb 2020 16:32:19 GMT
• Title: Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
• Authors: William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler
• Abstract summary: 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.
• Score: 34.837700505583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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