Article

Towards a theory of natural language interfaces to databases

Authors:

Ana-Maria Popescu,

Henry KautzAuthors Info & Claims

IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces

Pages 149 - 157

https://doi.org/10.1145/604045.604070

Published: 12 January 2003 Publication History

Abstract

The need for Natural Language Interfaces to databases (NLIs) has become increasingly acute as more and more people access information through their web browsers, PDAs, and cell phones. Yet NLIs are only usable if they map natural language questions to SQL queries correctly. As Schneiderman and Norman have argued, people are unwilling to trade reliable and predictable user interfaces for intelligent but unreliable ones. In this paper, we introduce a theoretical framework for reliable NLIs, which is the foundation for the fully implemented Precise NLI. We prove that, for a broad class of semantically tractable natural language questions, Precise is guaranteed to map each question to the corresponding SQL query. We report on experiments testing Precise on several hundred questions drawn from user studies over three benchmark databases. We find that over 80% of the questions are semantically tractable questions, which Precise answers correctly. Precise automatically recognizes the 20% of questions that it cannot handle, and requests a paraphrase. Finally, we show that Precise compares favorably with Mooney's learning NLI and with Microsoft's English Query product

References

[1]

J. Allen, G. Ferguson, and A. Stent. An architecture for more realistic conversational systems. In Intelligent User Interface, 2001.]]

Digital Library

[2]

I. Androutsopoulos, G. D. Ritchie, and P. Thanisch. Natural Language Interfaces to Databases - An Introduction. In Natural Language Engineering, vol 1, part 1, pages 29--81, 1995.]]

[3]

F. Baader and B. Hollunder. A Terminological Knowledge Representation System with Complete Inference Algorithms. In Proceedings of the First International Workshop on Processing Declarative Knowledge, 1991.]]

Digital Library

[4]

E. Charniak. A Maximum-Entropy-Inspired Parser. In Proceedings of NAACL-2000, 2000.]]

Digital Library

[5]

C. Chekuri and A. Rajamaran. Conjunctive Query Containment Revisited. In Proceedings of the Sixth International Conference on Database Theory, 1998.]]

Digital Library

[6]

W. Chu and F. Meng. Database Query Formation from Natural Language using Semantic Modeling and Statistical Keyword Meaning Disambiguation. Technical Report 990003, UCLA CS Dept., 16, 1999.]]

[7]

M. Core, J. Moore, and C. Zinn. Initiative in Tutorial Dialogue. In Proceedings of ITS 2002 Workshop on Empirical Methods for Tutorial Dialogue Systems (ITS-02), 2002.]]

[8]

T. Cormen, C. Leiserson, and R. Rivest. Introduction to Algorithms. MIT Press, Cambridge, Massachusetts, 1984.]]

Digital Library

[9]

F. Donini, M. Lenzerini, D. Nardi, and W. Nutt. The Complexity of Concept Languages. In Information and Computation, 134, 1997.]]

Digital Library

[10]

O. Etzioni, H. Kautz, and A. Popescu. Towards a theory of natural language interfaces to databases. In Submitted for publication, 2002.]]

[11]

B. Grosz, D. Appelt, P. Martin, and F. Pereira. TEAM: An Experiment in the Design of Transportable Natural Language Interfaces. In Artificial Intelligence 32, pages 173--243, 1987.]]

Digital Library

[12]

G. Hendrix, E. Sacerdoti, D. Sagalowicz, and J. Slocum. Developing a natural language interface to complex data. In ACM transactions on Database Systems 3(2), pages 105--147, 1978.]]

Digital Library

[13]

R. Kittredge. Sublanguages. In American Journal of Computational Linguistics 8(2), pages 79--84, 1982.]]

Digital Library

[14]

Kittredge.R. Variation and homogeneity of sublanguages. In R. Kittredge and J. Lehrberger, editors, Sublanguage: Studies of Language in Restricted Semantic Domains, pages 107--137. de Gruyter, Berlin, 1982.]]

[15]

G. R. Adaptive information extraction and sublanguage analysis. In Proceedings of IJCAI 2001, 2001.]]

[16]

S. Satingh, D. Litman, M. Kearns, and M. Walker. Optimizing Dialogue Management With Reinforcement Learning: Experiments with the NJFun System. In Journal of Artificial Intelligence Research (JAIR), 2002.]]

[17]

S. Satoshi. A New Direction For Sublanguage Nlp. In New Methods in Language Processing, pages 165--177, 1997.]]

[18]

D. Stallard. A terminological transformation for natural language question-answering systems. In Proceedings of ACL-86, 1986.]]

Digital Library

[19]

L. Tang and R. Mooney. Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing. In Proceedings of the 12th European Conference on Machine Learning (ECML-2001), Freiburg, Germany, pages 466--477, 2001.]]

Digital Library

[20]

T. Uno. Algorithms for Enumerating All Perfect, Maximum and Maximal Matchings in Bipartite Graphs. In International Symposium on Algorithms and Computation (ISAAC 1997), pages 92--101, 1997.]]

Digital Library

[21]

L. Valiant. A Theory of the Learnable. In Communications of the ACM, v27 n.11, pages 1134--1142, 1984.]]

Digital Library

[22]

A. Yates, O. Etzioni, and D. Weld. A Reliable Natural Language Interface to Household Appliances. In Submitted to IUI-2003, 2002.]]

Digital Library

Cited By

Zhang YDeriu JKatsogiannis-Meimarakis GKosten CKoutrika GStockinger K(2024)ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL SystemsProceedings of the VLDB Endowment10.14778/3636218.363622517:4(685-698)Online publication date: 5-Mar-2024
https://doi.org/10.14778/3636218.3636225
Li HZhang JLiu HFan JZhang XZhu JWei RPan HLi CChen H(2024)CodeS: Towards Building Open-source Language Models for Text-to-SQLProceedings of the ACM on Management of Data10.1145/36549302:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654930
Li QYou TChen JZhang YDu C(2024)LI-EMRSQL: Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical RecordsIEEE Transactions on Reliability10.1109/TR.2023.333633073:2(1280-1290)Online publication date: Jun-2024
https://doi.org/10.1109/TR.2023.3336330
Show More Cited By

Index Terms

Towards a theory of natural language interfaces to databases

Recommendations

Towards a theory of natural language interfaces to databases
IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces

The need for Natural Language Interfaces (NLIs) to databases has become increasingly acute as more nontechnical people access information through their web browsers, PDAs and cell phones. Yet NLIs are only usable if they map natural language questions ...
Interactive natural language interface

To override the complexity of SQL, and to facilitate the manipulation of data in databases for common people (not SQL professionals), many researches have turned out to use natural language instead of SQL. The idea of using natural language instead of ...
Generic interactive natural language interface to databases (GINLIDB)
EC'09: Proceedings of the 10th WSEAS international conference on evolutionary computing

To override the complexity of SQL, and to facilitate the manipulation of data in databases for common people (not SQL professionals), many researches have turned out to use natural language instead of SQL. The idea of using natural language instead of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces

January 2003

344 pages

ISBN:1581135866

DOI:10.1145/604045

Conference Chair:
David Leake
Indiana University
,
Program Chairs:
Lewis Johnson
USC/Information Sciences Institute
,
Elisabeth Andre
University of Augsburg

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 January 2003

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

IUI03

Sponsor:

IUI03: Eighth International Conference on Intelligent User Interfaces

January 12 - 15, 2003

Florida, Miami, USA

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

214
Total Citations
View Citations
2,355
Total Downloads

Downloads (Last 12 months)81
Downloads (Last 6 weeks)10

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang YDeriu JKatsogiannis-Meimarakis GKosten CKoutrika GStockinger K(2024)ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL SystemsProceedings of the VLDB Endowment10.14778/3636218.363622517:4(685-698)Online publication date: 5-Mar-2024
https://doi.org/10.14778/3636218.3636225
Li HZhang JLiu HFan JZhang XZhu JWei RPan HLi CChen H(2024)CodeS: Towards Building Open-source Language Models for Text-to-SQLProceedings of the ACM on Management of Data10.1145/36549302:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654930
Li QYou TChen JZhang YDu C(2024)LI-EMRSQL: Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical RecordsIEEE Transactions on Reliability10.1109/TR.2023.333633073:2(1280-1290)Online publication date: Jun-2024
https://doi.org/10.1109/TR.2023.3336330
Zhang WWang YSong YWei VTian YQi YChan JWong RYang H(2024)Natural Language Interfaces for Tabular Data Querying and Visualization: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340082436:11(6699-6718)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3400824
Yang CLiu YYin C(2024)More than a framework: Sketching out technical enablers for natural language-based source code generationComputer Science Review10.1016/j.cosrev.2024.10063753(100637)Online publication date: Aug-2024
https://doi.org/10.1016/j.cosrev.2024.100637
Pourreza MRafiei DOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)DIN-SQLProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667699(36339-36348)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667699
Ma CZhang WHuang MFeng SWu Y(2023)Integrating Relational Structure to Heterogeneous Graph for Chinese NL2SQL ParsersElectronics10.3390/electronics1209209312:9(2093)Online publication date: 4-May-2023
https://doi.org/10.3390/electronics12092093
John RBacon DChen JRamesh ULi JDas DClaus RKendall APatel JDas SPandis ISelçuk Candan KAmer-Yahia S(2023)DataChat: An Intuitive and Collaborative Data Analytics PlatformCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589678(203-215)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589678
Xusheng LYeteng AJingxian LHuimin ZYumeng ZMin LWei ZWei HLiangfei SHuiqin L(2023)Research on BERT-based Text2SQL Multi-task Learning2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA)10.1109/ICPECA56706.2023.10075969(864-868)Online publication date: 29-Jan-2023
https://doi.org/10.1109/ICPECA56706.2023.10075969
Diker SSakar C(2023)Creating CREATE queries with multi-task deep neural networksKnowledge-Based Systems10.1016/j.knosys.2023.110416266(110416)Online publication date: Apr-2023
https://doi.org/10.1016/j.knosys.2023.110416
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents