Tackling Complex Queries to Relational Databases

  • Octavian PopescuEmail author
  • Ngoc Phuoc An Vo
  • Vadim Sheinin
  • Elahe Khorashani
  • Hangu Yeo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)


Most people who want to get an answer from a structured repository, such as a database, are agnostic of both the formal language requested by database Structured Query Language (SQL), and of the particular structure of specific databases. On the other hand, processing arbitrary queries in natural language to automatically get the SQL is very challenging, especially due to the fact that most of the most frequent queries lead to Nested Logic Queries (NLQs). While most of the Natural Language Interface to Databases systems (NLIDB) may put severe restrictions on the form of the acceptable input queries, QUEST can deal with large variability in input. QUEST is a semi-supervised system which can encode the information about any database and process complex queries via an unsupervised learning methodology which addresses the problem of NLQs. We report a significant improvement in accuracy over other approaches.


  1. 1.
    Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD (2008)Google Scholar
  2. 2.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). Scholar
  3. 3.
    Kwiatkowski, T., Zettlemoyer, L.S., Goldwater, S., Steedman, M.: Lexical generalization in CCG grammar induction for semantic parsing. In: ACL, pp. 1512–1523 (2011)Google Scholar
  4. 4.
    Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: EMNLP (2013)Google Scholar
  5. 5.
    Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: ACL (2014)Google Scholar
  6. 6.
    Reddy, S., Lapata, M., Steedman, M.: Large-scale semantic parsing without question-answer pairs. Trans. Assoc. Comput. Linguist. 2, 377–392 (2014)CrossRefGoogle Scholar
  7. 7.
    Pustejovsky, J., Hanks, P., Rumshisky, A.: Automated induction of sense in context. In: Proceedings of the 20th International Conference on Computational Linguistics, Association for Computational Linguistics, p. 924 (2004)Google Scholar
  8. 8.
    Popescu, O., Tonelli, S., Pianta, E.: IRST-BP: preposition disambiguation based on chain clarifying relationships contexts. In: Proceedings of the 4th International Workshop on Semantic Evaluations, Association for Computational Linguistics, pp. 191–194 (2007)Google Scholar
  9. 9.
    Popescu, O.: Regular patterns-probably approximately correct language model. In: Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora, p. 12 (2013)Google Scholar
  10. 10.
    Kawahara, D., Peterson, D., Popescu, O., Palmer, M.: Inducing example-based semantic frames from a massive amount of verb uses. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 58–67 (2014)Google Scholar
  11. 11.
    Popescu, O., Hanks, P., Jezek, E., Kawahara, D.: Corpus patterns for semantic processing. In: Tutorials, pp. 12–15 (2015)Google Scholar
  12. 12.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)zbMATHGoogle Scholar
  13. 13.
    Marcus, M., et al.: The penn treebank: annotating predicate argument structure. In: Proceedings of the workshop on Human Language Technology, Association for Computational Linguistics, pp. 114–119 (1994)Google Scholar
  14. 14.
    Wang, Y., Berant, J., Liang, P.: Building a semantic parser overnight. In: ACL (2015)Google Scholar
  15. 15.
    Dong, L., Lapata, M.: Language to logical form with neural attention. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 August 2016, Berlin, Germany, vol. 1: Long Papers (2016)Google Scholar
  16. 16.
    Zelle, J.M., Mooney, R.J.: Learning to parse database queries using inductive logic programming. In: AAAI, pp. 1050–1055 (1996)Google Scholar
  17. 17.
    Copestake, A., Spärck Jones, K.: Inference in a natural language front end for databases. Technical report, University of Cambridge, Computer Laboratory (1989)Google Scholar
  18. 18.
    Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural language interfaces to databases - an introduction. Nat. Lang. Eng. 1(1), 29–81 (1995)CrossRefGoogle Scholar
  19. 19.
    Popescu, O.: Learning corpus patterns using finite state automata. In: Proceedings of the 10th International Conference on Computational Semantics, pp. 191–203 (2013)Google Scholar
  20. 20.
    Roth, D., Yih, W.: A linear programming formulation for global inference in natural language tasks. In: HLT-NAACL, pp. 1–8 (2004)Google Scholar
  21. 21.
    Punyakanok, V., Roth, D., Yih, W., Zimak, D.: Learning and inference over constrained output. In: IJCAI, vol. 5, pp. 1124–1129 (2005)Google Scholar
  22. 22.
    Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: EMNLP (2011)Google Scholar
  23. 23.
    Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O., Ang, S.D.: Parsing algebraic word problems into equations. TACL 3, 585–597 (2015)Google Scholar
  24. 24.
    Condoravdi, C., Richardson, K., Sikka, V., Suenbuel, A., Waldinger, R.: Natural language access to data: it takes common sense! In: 2015 AAAI Spring Symposium Series (2015)Google Scholar
  25. 25.
    Li, F., Jagadish, H.: Constructing an interactive natural language interface for relational databases. Proc. VLDB Endow. 8(1), 73–84 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Octavian Popescu
    • 1
    Email author
  • Ngoc Phuoc An Vo
    • 1
  • Vadim Sheinin
    • 1
  • Elahe Khorashani
    • 1
  • Hangu Yeo
    • 1
  1. 1.IBM ResearchYorktown HeightsUSA

Personalised recommendations