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In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed significant growth.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, pp. 1735–80, 12 1997
1997
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA . ACL, 2002, pp. 311–318. [Online]. Available: https://aclanthology.org/P02-1040/
2002
Earlier work this paper cites.
J. Gu, Z. Lu, H. Li, and V. O. K. Li, “Incorporating copying mechanism in sequence-to-sequence learning,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers . The Association for Computer Linguistics, 2016, pp. 1631–1640. [Online]. Available: https://doi.org/10.18653/v1/p16-1154
2016
Earlier work this paper cites.
J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin, “Convolutional sequence to sequence learning,” in Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 , ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. PMLR, 2017, pp. 1243–1252. [Online]. Available: http://proceedings.mlr.press/v70/gehring17a.html
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 5998–6008. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
2017
Earlier work this paper cites.
P. Trivedi, G. Maheshwari, M. Dubey, and J. Lehmann, “Lc-quad: A corpus for complex question answering over knowledge graphs,” in The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II , ser. Lecture Notes in Computer Science, C. d’Amato, M. Fernández, V. A. M. Tamma, F. Lécué, P. Cudré-Mauroux, J. F. Sequeda, C. Lange, and J. Heflin, Eds., vol. 10588. Springer, 2017, pp. 210–218. [Online]. Available: https://doi.org/10.1007/978-3-319-68204-4_22
2017
Earlier work this paper cites.
A. See, P. J. Liu, and C. D. Manning, “Get to the point: Summarization with pointer-generator networks,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers , R. Barzilay and M. Kan, Eds. Association for Computational Linguistics, 2017, pp. 1073–1083. [Online]. Available: https://doi.org/10.18653/v1/P17-1099
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
A.-K. Hartmann, T. Soru, and E. Marx, “Generating a large dataset for neural question answering over the dbpedia knowledge base,” 04 2018. [Online]. Available: https://www.researchgate.net/publication/324482598_Generating_a_Large_Dataset_for_Neural_Question_Answering_over_the_DBpedia_Knowledge_Base
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” 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) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019. [Online]. Available: https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
2019
Earlier work this paper cites.
M. Dubey, D. Banerjee, A. Abdelkawi, and J. Lehmann, “Lc-quad 2.0: A large dataset for complex question answering over wikidata and dbpedia,” in The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedings, Part II , ser. Lecture Notes in Computer Science, C. Ghidini, O. Hartig, M. Maleshkova, V. Svátek, I. F. Cruz, A. Hogan, J. Song, M. Lefrançois, and F. Gandon, Eds., vol. 11779. Springer, 2019, pp. 69–78. [Online]. Available: https://doi.org/10.1007/978-3-030-30796-7_5
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020 , D. Jurafsky, J. Chai, N. Schluter, and J. R. Tetreault, Eds. Association for Computational Linguistics, 2020, pp. 7871–7880. [Online]. Available: https://doi.org/10.18653/v1/2020.acl-main.703
2020
Cited alongside, same era.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. Mach. Learn. Res. , vol. 21, pp. 140:1–140:67, 2020. [Online]. Available: http://jmlr.org/papers/v21/20-074.html
2020
Cited alongside, same era.
X. Yin, D. Gromann, and S. Rudolph, “Neural machine translating from natural language to SPARQL,” Future Gener. Comput. Syst. , vol. 117, pp. 510–519, 2021. [Online]. Available: https://doi.org/10.1016/j.future.2020.12.013
2020
2022
Later among the works it cites.
D. Banerjee, P. A. Nair, J. N. Kaur, R. Usbeck, and C. Biemann, “Modern baselines for SPARQL semantic parsing,” in SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022 , E. Amigó, P. Castells, J. Gonzalo, B. Carterette, J. S. Culpepper, and G. Kazai, Eds. ACM, 2022, pp. 2260–2265. [Online]. Available: https://doi.org/10.1145/3477495.3531841
2022
Later among the works it cites.
S. Purkayastha, S. Dana, D. Garg, D. Khandelwal, and G. S. Bhargav, “A deep neural approach to kgqa via sparql silhouette generation,” in 2022 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2022, pp. 1–8
2022
Later among the works it cites.
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Cited alongside, same era.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds. Curran Associates, Inc., 2020, pp. 1877–1901. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
2020
Cited alongside, same era.
H. Tran, L. Phan, J. Anibal, B. T. Nguyen, and T.-S. Nguyen, “Spbert: an efficient pre-training bert on sparql queries for question answering over knowledge graphs,” in Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part I 28 . Springer, 2021, pp. 512–523
2021
Cited alongside, same era.
X. Huang, J.-J. Kim, and B. Zou, “Unseen entity handling in complex question answering over knowledge base via language generation,” in Findings of the Association for Computational Linguistics: EMNLP 2021 , 2021, pp. 547–557
2021
Cited alongside, same era.
X. Huang, J.-J. Kim, and B. Zou, “Unseen entity handling in complex question answering over knowledge base via language generation,” in Findings of the Association for Computational Linguistics: EMNLP 2021 . Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 547–557. [Online]. Available: https://aclanthology.org/2021.findings-emnlp.50
2021
Cited alongside, same era.
Y. Zhou, X. Geng, T. Shen, W. Zhang, and D. Jiang, “Improving zero-shot cross-lingual transfer for multilingual question answering over knowledge graph,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2021, pp. 5822–5834
2021
Cited alongside, same era.
2021
Cited alongside, same era.
R. Hirigoyen, A. Zouaq, and S. Reyd, “A copy mechanism for handling knowledge base elements in SPARQL neural machine translation,” in Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022 . Online only: Association for Computational Linguistics, Nov. 2022, pp. 226–236. [Online]. Available: https://aclanthology.org/2022.findings-aacl.22
2022
Cited alongside, same era.
J.-H. Lin and E. J.-L. Lu, “Sparql generation with an nmt-based approach,” Journal of Web Engineering , pp. 1471–1490, 2022
2022
Cited alongside, same era.
D. Banerjee, P. A. Nair, J. N. Kaur, R. Usbeck, and C. Biemann, “Modern baselines for sparql semantic parsing,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 2260–2265
2022
Cited alongside, same era.
2022
Later among the works it cites.
2023
Closest in time.
B. B. Naik, T. J. V. R. Reddy, K. R. V. karthik, and P. Kuila, “An sql query generator for cross-domain human language based questions based on nlp model,” Multimedia Tools and Applications , pp. 1–24, 2023
2023
Closest in time.
J. Lehmann, P. Gattogi, D. Bhandiwad, S. Ferré, and S. Vahdati, “Language models as controlled natural language semantic parsers for knowledge graph question answering,” in European Conference on Artificial Intelligence (ECAI) , vol. 372. IOS Press, 2023, pp. 1348–1356
2023
Closest in time.
2023
Closest in time.
2023
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S. Yang, M. Teng, X. Dong, and F. Bo, “Llm-based sparql generation with selected schema from large scale knowledge base,” in China Conference on Knowledge Graph and Semantic Computing . Springer, 2023, pp. 304–316
2023
Closest in time.
L. Kovriguina, R. Teucher, D. Radyush, and D. Mouromtsev, “Sparqlgen: One-shot prompt-based approach for sparql query generation,” 2023
2023
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2023
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T. Soru, E. Marx, D. Moussallem, G. Publio, A. Valdestilhas, D. Esteves, and C. B. Neto, “SPARQL as a foreign language,” in Proceedings of the Posters and Demos Track of the 13th International Conference on Semantic Systems - SEMANTiCS2017 co-located with the 13th International Conference on Semantic Systems (SEMANTiCS 2017), Amsterdam, The Netherlands, September 11-14, 2017 , ser. CEUR Workshop Proceedings, J. D. Fernández and S. Hellmann, Eds., vol. 2044. CEUR-WS.org, 2017. [Online]. Available: http://ceur-ws.org/Vol-2044/paper14/
2044
Closest in time.