2019/05/15 by William W. Cohen, Cohen, William W., M.A. Siegler +2
Computer Science · #Artificial Intelligence (cs.AI) #Databases (cs.DB) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1905.06209
openalex publication_date 2019/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.