Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler
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.
BibTeX
@inproceedings{
Cohen2020Scalable,
title={Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base},
author={William W. Cohen and Haitian Sun and R. Alex Hofer and Matthew Siegler},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=BJlguT4YPr}
}