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.
@article{arxiv.2306.14722,
title = {FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering},
author = {Lingxi Zhang and Jing Zhang and Yanling Wang and Shulin Cao and Xinmei Huang and Cuiping Li and Hong Chen and Juanzi Li},
journal= {arXiv preprint arXiv:2306.14722},
year = {2023}
}