@inproceedings{zhang-etal-2023-fc,
    title = "{FC}-{KBQA}: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering",
    author = "Zhang, Lingxi  and
      Zhang, Jing  and
      Wang, Yanling  and
      Cao, Shulin  and
      Huang, Xinmei  and
      Li, Cuiping  and
      Chen, Hong  and
      Li, Juanzi",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.57",
    doi = "10.18653/v1/2023.acl-long.57",
    pages = "1002--1017",
    abstract = "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. Our code is now available at GitHub \url{https://github.com/RUCKBReasoning/FC-KBQA}.",
}

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. Our code is now available at GitHub \url{https://github.com/RUCKBReasoning/FC-KBQA}.
Zhang, Lingxi Zhang, Jing Wang, Yanling Cao, Shulin Huang, Xinmei Li, Cuiping Chen, Hong Li, Juanzi
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering