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Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users.
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Semantic parsing on freebase from question-answer pairs
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Distilling the Knowledge in a Neural Network
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Su, Y.; Sun, H.; Sadler, B.; Srivatsa, M.; Gür, I.; Yan, Z.; and Yan, X. 2016 · 2016
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Yih, W.-t.; Richardson, M.; Meek, C.; Chang, M.-W.; and Suh, J. 2016 · 2016
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Gao, T.; Han, X.; Zhu, H.; Liu, Z.; Li, P.; Sun, M.; and Zhou, J. 2019 · 2019
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Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases
Lan, Y.; and Jiang, J. 2020 · 2020
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Case-based Reasoning for Natural Language Queries over Knowledge Bases
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A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions
Lan, Y.; He, G.; Jiang, J.; Jiang, J.; Zhao, W. X.; and Wen, J.-R. 2021 · 2021
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Effective few-shot named entity linking by meta-learning
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TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
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KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
Cao, S.; Shi, J.; Pan, L.; Nie, L.; Xiang, Y.; Hou, L.; Li, J.; He, B.; and Zhang, H. 2022a
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Program transfer for answering complex questions over knowledge bases
Cao, S.; Shi, J.; Yao, Z.; Lv, X.; Yu, J.; Hou, L.; Li, J.; Liu, Z.; and Xiao, J. 2022b
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