18902

Neural Query Language: A Knowledge Base Query Language for Tensorflow

William W. Cohen, Matthew Siegler, Alex Hofer
Google Research
arXiv:1905.06209 [cs.LG], (15 May 2019)

@misc{cohen2019neural,

   title={Neural Query Language: A Knowledge Base Query Language for Tensorflow},

   author={William W. Cohen and Matthew Siegler and Alex Hofer},

   year={2019},

   eprint={1905.06209},

   archivePrefix={arXiv},

   primaryClass={cs.LG}

}

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.
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