The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained modeling of the logical expression, or inexecutability issues due to the fine-grained modeling of disconnected classes and relations in real KBs. We propose a Fine-to-Coarse Composition framework for KBQA (FC-KBQA) to both ensure the generalization ability and executability of the logical expression. The main idea of FC-KBQA is to extract relevant fine-grained knowledge components from KB and reformulate them into middle-grained knowledge pairs for generating the final logical expressions. FC-KBQA derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than the baseline.

翻译：知识库问答的泛化问题已引起广泛关注。现有研究受限于逻辑表达式粗粒度建模带来的纠缠泛化问题，或由于真实知识库中非连通类别与关系的细粒度建模导致的可执行性问题。我们提出一种面向知识库问答的由细到粗组合框架（FC-KBQA），以兼顾逻辑表达式的泛化能力与可执行性。FC-KBQA的核心思想是从知识库中提取相关细粒度知识组件，并将其重组为中粒度知识对，用于生成最终逻辑表达式。FC-KBQA在GrailQA和WebQSP数据集上取得了新的最优性能，且运行速度比基线方法快4倍。