Complex Knowledge Base Question Answering via Structure and Content Dual-Driven Method
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-7235-3_16
Reference18 articles.
1. Banerjee, D., Nair, P.A., Usbeck, R., Biemann, C.: GETT-QA: graph embedding based t2t transformer for knowledge graph question answering. In: Pesquita, C., et al. (eds.) European Semantic Web Conference, vol. 13870, pp. 279–297. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-33455-9_17
2. Cao, X., Liu, Y.: Relmkg: reasoning with pre-trained language models and knowledge graphs for complex question answering. Appl. Intell. 53(10), 12032–12046 (2023)
3. Chen, S., Liu, Q., Yu, Z., Lin, C.Y., Lou, J.G., Jiang, F.: Retrack: a flexible and efficient framework for knowledge base question answering. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pp. 325–336 (2021)
4. Chen, Y., Li, H., Hua, Y., Qi, G.: Formal query building with query structure prediction for complex question answering over knowledge base. arXiv preprint arXiv:2109.03614 (2021)
5. Cui, H., Peng, T., Bao, T., Han, R., Han, J., Liu, L.: Stepwise relation prediction with dynamic reasoning network for multi-hop knowledge graph question answering. Appl. Intell. 53(10), 12340–12354 (2023)
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