Advertisement

Natural Language Database Access Using Semi-automatically Constructed Translation Knowledge

  • In-Su Kang
  • Jae-Hak J. Bae
  • Jong-Hyeok Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)

Abstract

In most natural language database interfaces (NLDBI), translation knowledge acquisition heavily depends on human specialties, consequently undermining domain portability. This paper attempts to semi-automatically construct translation knowledge by introducing a physical Entity-Relationship schema, and by simplifying translation knowledge structures. Based on this semi-automatically produced translation knowledge, a noun translation method is proposed in order to resolve NLDBI translation ambiguities.

Keywords

Translation Knowledge Domain Class Class Term Case Marker User Question 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Androutsopoulos, I.: Interfacing a Natural Language Front-End to Relational Database. Master.s thesis, Technical Report 11, Department of Artificial Intelligence, University of Edinburgh (1993)Google Scholar
  2. 2.
    Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural Language Interfaces to Databases. An Introduction. Natural Language Engineering 1(1), 29–81 (1995)CrossRefGoogle Scholar
  3. 3.
    Copeck, T., Barker, K., Delisle, S., Szpakowicz, S., Delannoy, J.F.: What is Technical Text? Language Sciences 19(4), 391–424 (1997)CrossRefGoogle Scholar
  4. 4.
    Damerau, F.: Problems and Some Solutions in Customization of Natural Language Database Front Ends. ACM Transactions on Office Information Systems 3(2), 165–184 (1985)CrossRefGoogle Scholar
  5. 5.
    Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: The vocabulary problem in human-system communication. Communications of the ACM 30(11), 964–971 (1987)CrossRefGoogle Scholar
  6. 6.
    Grosz, B.J., Appelt, D.E., Martin, P.A., Pereira, F.C.N.: TEAM: An Experiment in the Design of Transportable Natural-Language Interfaces. Artificial Intelligence 32(2), 173–243 (1987)CrossRefGoogle Scholar
  7. 7.
    Hendrix, G.G., Sacerdoti, D., Sagalowicz, D., Slocum, J.: Developing a Natural Language Interface to Complex Data. ACM Transactions on Database Systems 3(2), 105–147 (1978)CrossRefGoogle Scholar
  8. 8.
    Lee, H.D., Park, J.C.: Interpretation of Natural language Queries for Relational Database Access with Combinatory Categorial Grammar. International Journal of Computer Processing of Oriental Languages 15(3), 281–304 (2002)CrossRefGoogle Scholar
  9. 9.
    Meng, F., Chu, W.W.: Database Query Formation from Natural Language using Semantic Modeling and Statistical Keyword Meaning Disambiguation. Technical Report, CSD-TR 990003,University of California, Los Angeles (1999)Google Scholar
  10. 10.
    Salton, G., McGill, M.J.: An Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)Google Scholar
  11. 11.
    Wallace, M.: Communicating with Databases in Natural Language. Ellis Horwood, Chichester (1984)Google Scholar
  12. 12.
    Waltz, D.L.: An English Language Question Answering System for a Large Relational Database. Communications of the ACM 21(7), 526–539 (1978)zbMATHCrossRefGoogle Scholar
  13. 13.
    Warren, D., Pereira, F.: An Efficient Easily Adaptable System for Interpreting Natural Language Queries. Computational Linguistics 8(3-4), 110–122 (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • In-Su Kang
    • 1
  • Jae-Hak J. Bae
    • 2
  • Jong-Hyeok Lee
    • 1
  1. 1.Div. of Electrical and Computer EngineeringPOSTECH and AITrcPohangRepublic of Korea
  2. 2.School of Computer Engineering and Information TechnologyUniversity of UlsanRepublic of Korea

Personalised recommendations