{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:40:56Z","timestamp":1774456856023,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["No.: 2020YFS0090, 2020ZHCG0078, 2021YFN0117, 2022YFS0135"],"award-info":[{"award-number":["No.: 2020YFS0090, 2020ZHCG0078, 2021YFN0117, 2022YFS0135"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Medico Engineering Cooperation Funds from university of Electronic Science and Technology of China","award":["No.: ZYGX2021YGLH011"],"award-info":[{"award-number":["No.: ZYGX2021YGLH011"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s10489-023-05221-z","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T09:02:03Z","timestamp":1704704523000},"page":"1511-1524","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["NL2SQL with partial missing metadata based on multi-view metadata graph compensation and reasoning"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3476-7536","authenticated-orcid":false,"given":"Jie","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"5221_CR1","doi-asserted-by":"publisher","unstructured":"Katsogiannis-Meimarakis G, Koutrika G (2023) A survey on deep learning approaches for text-to-sql. VLDB J, 1\u201332. https:\/\/doi.org\/10.1007\/s00778-022-00776-8","DOI":"10.1007\/s00778-022-00776-8"},{"key":"5221_CR2","doi-asserted-by":"publisher","unstructured":"Yu T, Zhang R, Yang K, Yasunaga M, Wang D, Li Z, Ma J, Li I, Yao Q, Roman S et al (2018) Spider: a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3911\u20133921. https:\/\/doi.org\/10.18653\/v1\/d18-1425","DOI":"10.18653\/v1\/d18-1425"},{"key":"5221_CR3","doi-asserted-by":"publisher","unstructured":"Yu T, Zhang R, Yasunaga M, Tan YC, Lin XV, Li S, Er H, Li I, Pang B, Chen T et al (2019) Sparc: cross-domain semantic parsing in context. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 4511\u20134523. https:\/\/doi.org\/10.18653\/v1\/p19-1443","DOI":"10.18653\/v1\/p19-1443"},{"key":"5221_CR4","doi-asserted-by":"publisher","unstructured":"Scholak T, Schucher N, Bahdanau D (2021) Picard: parsing incrementally for constrained auto-regressive decoding from language models. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 9895\u20139901. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.779","DOI":"10.18653\/v1\/2021.emnlp-main.779"},{"key":"5221_CR5","doi-asserted-by":"publisher","unstructured":"Hui B, Geng R, Wang L, Qin B, Li Y, Li B, Sun J, Li Y (2022) S2sql: injecting syntax to question-schema interaction graph encoder for text-to-sql parsers. In: Findings of the association for computational linguistics: ACL 2022, pp 1254\u20131262. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.9","DOI":"10.18653\/v1\/2022.findings-acl.9"},{"key":"5221_CR6","doi-asserted-by":"publisher","unstructured":"Qi J, Tang J, He Z, Wan X, Cheng Y, Zhou C, Wang X, Zhang Q, Lin Z (2022) Rasat: integrating relational structures into pretrained seq2seq model for text-to-sql. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 3215\u20133229. https:\/\/doi.org\/10.48550\/arXiv.2205.06983","DOI":"10.48550\/arXiv.2205.06983"},{"key":"5221_CR7","doi-asserted-by":"publisher","unstructured":"Wang B, Shin R, Liu X, Polozov O, Richardson M (2020) Rat-sql: relation-aware schema encoding and linking for text-to-sql parsers. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 7567\u20137578. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.677","DOI":"10.18653\/v1\/2020.acl-main.677"},{"key":"5221_CR8","doi-asserted-by":"publisher","unstructured":"Rubin O, Berant J (2021) Smbop: semi-autoregressive bottom-up semantic parsing. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 311\u2013324. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.290","DOI":"10.18653\/v1\/2021.naacl-main.290"},{"key":"5221_CR9","doi-asserted-by":"publisher","unstructured":"Cao R, Chen L, Chen Z, Zhao Y, Zhu S, Yu K (2021) Lgesql: line graph enhanced text-to-sql model with mixed local and non-local relations. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers), pp 2541\u20132555. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.198","DOI":"10.18653\/v1\/2021.acl-long.198"},{"key":"5221_CR10","doi-asserted-by":"publisher","unstructured":"Lin XV, Socher R, Xiong C (2020) Bridging textual and tabular data for cross-domain text-to-sql semantic parsing. In: Findings of the association for computational linguistics: EMNLP 2020, pp 4870\u20134888. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.438","DOI":"10.18653\/v1\/2020.findings-emnlp.438"},{"key":"5221_CR11","doi-asserted-by":"publisher","unstructured":"Guo J, Zhan Z, Gao Y, Xiao Y, Lou J-G, Liu T, Zhang D (2019) Towards complex text-to-sql in cross-domain database with intermediate representation. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 4524\u20134535. https:\/\/doi.org\/10.18653\/v1\/p19-1444","DOI":"10.18653\/v1\/p19-1444"},{"key":"5221_CR12","doi-asserted-by":"publisher","unstructured":"Dong L, Lapata M (2018) Coarse-to-fine decoding for neural semantic parsing. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 731\u2013742. https:\/\/doi.org\/10.18653\/v1\/p18-1068","DOI":"10.18653\/v1\/p18-1068"},{"key":"5221_CR13","doi-asserted-by":"publisher","unstructured":"Chen Z, Chen L, Zhao Y, Cao R, Xu Z, Zhu S, Yu K (2021) Shadowgnn: graph projection neural network for text-to-sql parser. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 5567\u20135577. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.441","DOI":"10.18653\/v1\/2021.naacl-main.441"},{"key":"5221_CR14","doi-asserted-by":"publisher","unstructured":"Hui B, Geng R, Ren Q, Li B, Li Y, Sun J, Huang F, Si L, Zhu P, Zhu X (2021) Dynamic hybrid relation exploration network for cross-domain context-dependent semantic parsing. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 13116\u201313124. https:\/\/doi.org\/10.1609\/aaai.v35i14.17550","DOI":"10.1609\/aaai.v35i14.17550"},{"key":"5221_CR15","doi-asserted-by":"publisher","unstructured":"Khan MR, Blumenstock JE (2019) Multi-gcn: graph convolutional networks for multi-view networks, with applications to global poverty. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 606\u2013613. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301606","DOI":"10.1609\/aaai.v33i01.3301606"},{"key":"5221_CR16","doi-asserted-by":"publisher","unstructured":"Ishiwatari T, Yasuda Y, Miyazaki T, Goto J (2020) Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 7360\u20137370. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.597","DOI":"10.18653\/v1\/2020.emnlp-main.597"},{"key":"5221_CR17","doi-asserted-by":"publisher","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Berg Rvd, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference. Springer, pp 593\u2013607 https:\/\/doi.org\/10.7287\/peerj-cs.1073v0.2\/reviews\/2","DOI":"10.7287\/peerj-cs.1073v0.2\/reviews\/2"},{"key":"5221_CR18","doi-asserted-by":"publisher","unstructured":"Li S, Li W-T, Wang W (2020) Co-gcn for multi-view semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 4691\u20134698. https:\/\/doi.org\/10.1609\/aaai.v34i04.5901","DOI":"10.1609\/aaai.v34i04.5901"},{"key":"5221_CR19","doi-asserted-by":"publisher","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI conference on artificial intelligence. https:\/\/doi.org\/10.1609\/aaai.v29i1.9491","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"5221_CR20","doi-asserted-by":"publisher","unstructured":"Yu T, Yasunaga M, Yang K, Zhang R, Wang D, Li Z, Radev D (2018) Syntaxsqlnet: syntax tree networks for complex and cross-domain text-to-sql task. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 1653\u20131663. https:\/\/doi.org\/10.18653\/v1\/d18-1193","DOI":"10.18653\/v1\/d18-1193"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05221-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05221-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05221-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T12:22:44Z","timestamp":1708086164000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05221-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":20,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["5221"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05221-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"6 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}