  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.
