Large knowledge bases (KBs) are useful for many AI tasks, but are difficult to integrate into modern gradient-based learning systems. Here we describe a framework for accessing soft symbolic database using only differentiable operators. For example, this framework makes it easy to conveniently write neural models that adjust confidences associated with facts in a soft KB; incorporate prior knowledge in the form of hand-coded KB access rules; or learn to instantiate query templates using information extracted from text. NQL can work well with KBs with millions of tuples and hundreds of thousands of entities on a single GPU.
@article{arxiv.1905.06209,
title = {Neural Query Language: A Knowledge Base Query Language for Tensorflow},
author = {William W. Cohen and Matthew Siegler and Alex Hofer},
journal= {arXiv preprint arXiv:1905.06209},
year = {2019}
}