Self-supervised BGP-graph reasoning enhanced complex KBQA via SPARQL generation

Author:

Gao FengORCID,Yang YanORCID,Gao PengORCID,Gu MingORCID,Zhao ShangqingORCID,Chen Yuefeng,Yuan Hao,Lan Man,Zhou AiminORCID,He LiangORCID

Publisher

Elsevier BV

Reference51 articles.

1. Bai, X., Chen, Y., & Zhang, Y. (2022). Graph Pre-training for AMR Parsing and Generation. In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers) (pp. 6001–6015). http://dx.doi.org/10.18653/v1/2022.acl-long.415.

2. Boosting question answering over knowledge graph with reward integration and policy evaluation under weak supervision;Bi;Information Processing & Management,2023

3. Cao, S., Shi, J., Pan, L., Nie, L., Xiang, Y., Hou, L., et al. (2022). KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base. In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers) (pp. 6101–6119). http://dx.doi.org/10.18653/v1/2022.acl-long.422.

4. Cao, S., Shi, J., Yao, Z., Lv, X., Yu, J., Hou, L., et al. (2022). Program Transfer for Answering Complex Questions over Knowledge Bases. In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers) (pp. 8128–8140). http://dx.doi.org/10.18653/v1/2022.acl-long.559.

5. Chen, Z.-Y., Chang, C.-H., Chen, Y.-P., Nayak, J., & Ku, L.-W. (2019). UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 345–356). http://dx.doi.org/10.18653/v1/N19-1031.

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