Features and Pitfalls that Users Should Seek in Natural Language Interfaces to Databases

  • Rodolfo A. Pazos Rangel
  • Marco A. Aguirre
  • Juan J. González
  • Juan Martín Carpio
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 547)

Abstract

Natural Language Interfaces to Databases (NLIDBs) are tools that can be useful in making decisions, allowing different types of users to get information they need using natural language communication. Despite their important features and that for more than 50 years NLIDBs have been developed, their acceptance by end users is very low due to extremely complex problems inherent to natural language, their customization and internal operation, which has produced poor performance regarding queries correctly translated. This chapter presents a study on the main desirable features that NLIDBs should have as well as their pitfalls, describing some study cases that occur in some interfaces to illustrate the flaws of their approach.

References

  1. 1.
    Pazos, R., González, J., Aguirre, M., Martínez, J., Fraire, H.: Natural language interfaces to databases: an analysis of the state of the art. Recent Adv. Hybrid Intell. Syst. Stud. Comput. Intell. 451, 463–480 (2013)CrossRefGoogle Scholar
  2. 2.
    Reis, P., Mamede, N., Matias, J.: Edite: a natural language interface to databases: a new dimension for an old approach. In: Proceedings of the 4th International Conference on Information and Communication Technology in Tourism (1997)Google Scholar
  3. 3.
    Pazos, R., González, J., Aguirre, M.: Semantic model for improving the performance of natural language interfaces. In: Proceedings of the MICAI 2011 Mexican International Conference on Advances in Artificial Intelligence, pp. 277–290 (2011)Google Scholar
  4. 4.
    Jain, H.: Hindi language interface to databases. Master’s thesis, Thapar University (2011)Google Scholar
  5. 5.
    Kovacs, L.: SQL generation for natural language interface. J. Comput. Sci. Control Syst. 2(18), 19–22 (2009)Google Scholar
  6. 6.
    Meng, X., Wang, S.: NChiql: the Chinese natural language interface to databases. Lecture Notes in Computer Science 2113, pp. 145–154 (2001)Google Scholar
  7. 7.
    Boldasov, M., Sokolova, E., Malkovsky, M.: User query understanding by the InBASE system as a source for a multilingual NL generation module. Lect. Notes Comput. Sci. 2448, 33–40 (2002)CrossRefGoogle Scholar
  8. 8.
    Jung, H., Geunbae, G.: Multilingual question answering with high portability on relational databases. In: Proceedings Conference on Multilingual Summarization and Question Answering 19, pp. 1–8 (2002)Google Scholar
  9. 9.
    Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., Steedman, M.: Inducing probabilistic CCG grammars from logical form with higher-order unification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1223–1233 (2010)Google Scholar
  10. 10.
    Padró Ll., Stanilovsky, E.: FreeLing 3.0: towards wider multilinguality. In: Proceedings of the Language Resources and Evaluation Conference (2012)Google Scholar
  11. 11.
    Pakray, P.: Keyword based multilingual restricted domain question answering. Master’s thesis, Jadavpur University (2007)Google Scholar
  12. 12.
    Hallet, C., Scott, D., Power, R.: Composing questions through conceptual authoring. Comput. Linguist. 33, 105–133 (2007)CrossRefGoogle Scholar
  13. 13.
    Tang, L., Mooney, R.: Using multiple clause constructors in inductive logic programming for semantic parsing. In: Proceedings of 12th European Conference on Machine Learning, pp. 466–477 (2001)Google Scholar
  14. 14.
    Price, P.: Evaluation of spoken language systems: the ATIS domain. In: Proceedings of the DARPA Speech and Natural Language Workshop, pp. 91–95Google Scholar
  15. 15.
    Clarke, J., Goldwasser, D., Chang, M.W., Roth, D.: Driving semantic parsing from the world’s response. In: Proceedings 14th Conference on Computational Natural Language Learning, pp. 18–27 (2010)Google Scholar
  16. 16.
    Giordani, A., Moschitti, A.: Translating questions to SQL queries with generative parsers discriminatively reranked. In: Proceedings of the Conference on Computational Linguistics (Posters), 401–410 (2012)Google Scholar
  17. 17.
    Kate, R., Mooney, R.: Using string-kernels for learning semantic parsers. In Proceedings of 21st ICCL and 44th Annual Meeting of the Association for Computational Linguistics, pp. 913–920 (2006)Google Scholar
  18. 18.
    Liang, P., Jordan, M., Klein, D.: Learning dependency-based compositional semantics. In: Proceedings of 49th Annual Meeting of the Association for Computational Linguistics, pp. 590–599 (2011)Google Scholar
  19. 19.
    Lu, W., Tou, H.N., Lee, W.S., Zettlemoyer, L.: A generative model for parsing natural language to meaning representations. In Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 783–792 (2008)Google Scholar
  20. 20.
    Kaufman, E., Bernstein, A., Fischer, L.: NLP-Reduce: A “naïve” but domain-independent natural language interface for querying ontologies. In: Proceedings of the 4th European Semantic Web Conference, pp. 1–2 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rodolfo A. Pazos Rangel
    • 1
  • Marco A. Aguirre
    • 1
  • Juan J. González
    • 1
  • Juan Martín Carpio
    • 2
  1. 1.Instituto Tecnológico de Ciudad MaderoCiudad MaderoMexico
  2. 2.Instituto Tecnológico de LeónLeónMexico

Personalised recommendations