It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the ComplexWebQuestions dataset, CBR-KBQA outperforms the current state of the art by 11\% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases \emph{without} any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.

翻译：解决一个从零开始的复杂问题往往具有挑战性,但如果我们能够从零到零地获得其他类似问题的解决办法 -- -- 一个称为基于案例的推理(CBR)的范例 -- -- 解决这些复杂问题往往会更容易得多。我们提议一种神经-共振的CBR(CBR-KBQA)方法(CBR-KBQA)用于在大型知识库中解答问题。CBR-KBQA包含一个非参数记忆,存储案例(问题和逻辑形式)和一个参数模型,可以通过检索与之相关的案例,为新问题产生一种逻辑形式。在包含复杂问题的多个 KBQA数据集中,CBR-KBQA取得了竞争性的性能。例如,在复杂的网上问题数据集中,CBR-KBQA在准确性方面超越了目前艺术状态。此外,我们证明CBR-KQA能够使用新的案例\emph{而不经过任何进一步的培训:通过在案例记忆中加入几个人类标注的例子,CBR-KQA能够成功地生成含有隐形KBB实体的逻辑形式,作为关系的逻辑关系。