FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering

• URL: http://arxiv.org/abs/2306.14722v1
• Date: Mon, 26 Jun 2023 14:19:46 GMT
• Title: FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering
• Authors: Lingxi Zhang, Jing Zhang, Yanling Wang, Shulin Cao, Xinmei Huang, Cuiping Li, Hong Chen, Juanzi Li
• Abstract summary: We propose a Fine-to-Coarse Composition framework for KBQA (FC-KBQA) to ensure the generalization ability and executability of the logical expression. FC-KBQA derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than the baseline.
• Score: 24.394908238940904
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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