[
Aho, A.V., R. Sethi, and J.D. Ullman. 1986. Compilers: principles, techniques, and tools. Boston: Addison-Wesley Longman Publishing Co.
]Search in Google Scholar
[
Andrews, P.R. 2002. An Introduction to Mathematical Logic and Type Theory: To Truth Through Proof. Dordrecht: Kluwer Academic Publications.
]Search in Google Scholar
[
Axmark, D., and D. Widenius. 2021. MySQL 8.0 Reference Manual. Redwood Shores: Oracle Corporation. Available at: http://dev.mysql.com/doc/refman/8.0/en/ (accessed January 2023).
]Search in Google Scholar
[
Barendregt, H.P. 1984. The Lambda Calculus; Its Syntax and Semantics. Amsterdam: Elsevier Science B.V.
]Search in Google Scholar
[
Ben-Gan, I., and T. Moreau. 2000. Advanced Transact-SQL for SQL Server 2000. New York: Springer-Verlag.
]Search in Google Scholar
[
Bojanowski, P., E. Grave, A. Joulin, and T. Mikolov. 2017. “Enriching Word Vectors with Subword Information.” Transactions of the Association for Computational Linguistics 5: 135–146. DOI: https://doi.org/10.1162/tacl_a_00051.
]Search in Google Scholar
[
Brickley, D., and R.V. Guha. 2014. RDF Schema 1.1. Massachusetts: W3C. Available at: http://www.w3.org/TR/2014/REC-rdf-schema-20140225/ (accessed January 2023).
]Search in Google Scholar
[
Chollet, F. 2018. Deep Learning with Python. Shelter Island: Manning Publications Co.
]Search in Google Scholar
[
Codd, E.F. 1970. “A Relational Model of Data for Large Shared Data Banks.” Communications of the ACM 13: 377–387. DOI: https://doi.org/10.1145/362384.362685.
]Search in Google Scholar
[
Enderle, T., S. Giessing, and R. Tent. 2006. “Designing Confidentiality on the Fly Methodology – Three Aspects.” Proceedings of PSD LNCS 11126: 28–42. DOI: https://doi.org/10.1007/978-3-319-99771-1_3.
]Search in Google Scholar
[
Fraser B, and J. Wooton. 2006. “A proposed method for confidentialising tabular output to protect against differencing.” Monographs of Official Statistics. Work session on Statistical Data Confidentiality: 299–302. Luxembourg: Eurostat-Office for Official Publications of the European Communities. Corpus ID: 53573926.
]Search in Google Scholar
[
Gelsema, T. 2008. “General requirements for the soundness of metadata models.” Joint UNECE/Eurostat/OECD work session on statistical metadata (METIS). Available at: https://www.researchgate.net/publication/334708026_General_requirements_for_the_soundness_of_metadata_models (accessed January 2023).
]Search in Google Scholar
[
Gelsema, T. 2012. “The Organization of Information in a Statistical Office.” Journal of Official Statistics 28(3): 413–440. Available at: https://www.scb.se/contentassets/-ca21efb41fee47d293bbee5bf7be7fb3/the-organization-of-information-in-a-statistical-office.pdf.
]Search in Google Scholar
[
Gelsema, T. 2019. “The Logic of Aggregated Data.” Acta Cybernetica 24(2): 211–248. DOI: https://doi.org/10.14232/actacyb.24.2.2019.4.
]Search in Google Scholar
[
Geron, A. 2017. Hands-On Machine Learning with Scikit-Learn & Tensorflow. Sebastopol: O’Reilly Media Inc.
]Search in Google Scholar
[
Guo, J., Z. Zhan, Y. Xiao, J.G. Lou, T. Liu and D. Zhang. 2019. “Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation.” DOI: https://doi.org/10.48550/arXiv.1905.08205.
]Search in Google Scholar
[
Hayes, P.J., and P.F. Patel-Schneider. 2014. RDF 1.1 Semantics. Massachusetts: W3C. Available at: http://www.w3.org/TR/2014/REC-rdf11-mt-20140225/ (accessed January 2023).
]Search in Google Scholar
[
Indurkhya, N., and F.J. Damerau. 2010. Handbook of Natural Language Processing, Second Edition. Boca Raton: Chapman & Hall/CRC.
]Search in Google Scholar
[
Ji, Z., Z.C. Lipton and C. Elkan. 2014. “Differential privacy and machine learning: a survey and review.” DOI: https://doi.org/10.48550/arXiv.1412.7584.
]Search in Google Scholar
[
Katsogiannis-Meimarakis, G., and G. Koutrika. 2021. “Deep Learning Approaches for Text-to-SQL Systems.” In Proceedings of the 24th International Conference on Extending Database Technology (EDBT), March 23–26: 710–713. Nicosia. Available at: https://openproceedings.org/2021/conf/edbt/p306.pdf (accessed January 2023).
]Search in Google Scholar
[
Kennet, R.S., and G. Shmueli. 2016. “From Quality to Information Quality in Official Statistics.” Journal of Official Statistics 32(4): 867–885. DOI: https://doi.org/10.1515/-jos-2016-0045.
]Search in Google Scholar
[
Marley, J.K., and V.L. Leaver. 2011. “A method for confidentialising user-defined tables: statistical properties and a risk-utility analysis.” In Proceedings of 58th World Statistical Congress: International Statistical Institute, Dublin. Available at: https://2011.isiproceedings.org (accessed January 2023).
]Search in Google Scholar
[
Meinke, K., and J.V. Tucker. 1992. “Universal Algebra.” In Handbook of Logic in Computer Science, Vol. I: Background; Mathematical Structures edited by S. Abramsky, M. Gabbay and T. Maibaum: 189–411. Oxford: Oxford Science Publications.
]Search in Google Scholar
[
Mikolov, T., K. Chen, G. Corrado, and J. Dean. 2013. “Efficient Estimation of Word Representations in Vector Space.” DOI: https://doi.org/10.48550/arXiv.1301.3781.
]Search in Google Scholar
[
Motik, B., P.F. Patel-Schneider and B. Cuenca Grau. 2012. OWL 2 Web Ontology Language Direct Semantics (Second Edition). Massachusetts: W3C. Available at: http://www.w3.org/TR/2012/REC-owl2-direct-semantics-20121211/ (accessed January 2023).
]Search in Google Scholar
[
Saha, D., A. Floratou, K. Sankaranarayanan, U. Farooq Minhas, A.R. Mittal and F. Ozcan. 2016. “ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores.” In Proceedings of the VLDB Endowment 9(12): 1209–1220. Available at https://vldb.org/pvldb/vol9/p1209-saha.pdf (accessed January 2023).
]Search in Google Scholar
[
Thompson, G., S. Broadfoot, and D. Elazar. 2013. “Methodology for the automatic confidentialisation of statistical outputs from remote servers at the Autralian Bureau of Statistics.” Joint UNECE/Eurostat Work Session on Statistical Data Confidentiality, October 28–30. Ottawa. Available at: https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf (accessed January 2023).
]Search in Google Scholar
[
Vasiliev, Y. 2020. Natural Language Processing with Python and spaCy, A Practical Introduction. San Francisco: No Starch Press.
]Search in Google Scholar
[
Wang, B., R. Shin, X. Liu, O. Polozov, and M. Richardso. 2020. “RAT-SQL: Relation-Aware Schema Encoding, and Linking for Text-to-SQL Parsers.” DOI: https://doi.org/10.48550/arXiv.1911.04942.
]Search in Google Scholar
[
Weir, N., P. Utama, A. Galakatos, and A. Crotty. 2020. “DBPal: A Fully Pluggable NL2SQL Training Pipeline.” In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, June 14–19: 2347–2361. New York: Association for Computing Machinery. Available at: https://dl.acm.org/doi/10.1145/3318464.3380589 (accessed January 2023).
]Search in Google Scholar
[
Xu, X., C. Liu, and D. Song. 2017. “SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning.” DOI: https://doi.org/10.48550/arXiv.1711.04436.
]Search in Google Scholar
[
Zhong, V., C. Xiong, and R. Socher. 2017. “Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning.” DOI: https://doi.org/10.48550/arXiv.1709.00103.
]Search in Google Scholar