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.
翻译:大型知识库(KB)对于许多AI任务有用,但难以融入现代梯度学习系统。 我们在这里描述一个仅使用不同操作员访问软象征性数据库的框架。 例如, 这个框架方便地写入神经模型, 以调整软KB中与事实相关的信任; 以手码KB访问规则的形式纳入先前的知识; 或学习利用从文本中提取的信息对查询模板进行即时处理。 NQL 与千千万万个图普和数以万计的实体一起在单一的GPU上运作。