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Natural language interfaces to databases – an introduction
Open accessAbstract
This paper is an introduction to natural language interfaces to databases (NLIDBS). A brief overview of the history of NLIDBS is first given. Some advantages and disadvantages of NLIDBS are then discussed, comparing NLIDBS to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems NLIDBS have to confront follows, for the benefit of readers less familiar with computational linguistics. The discussion then moves on to NLIDB architectures, portability issues, restricted natural language input systems (including menu-based NLIDBS), and NLIDBS with reasoning capabilities. Some less explored areas of NLIDB research are then presented, namely database updates, meta-knowledge questions, temporal questions, and multi-modal NLIDBS. The paper ends with reflections on the current state of the art.
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“E-learning and mental health represent relevant interaction contexts, which have also seen great prominence in recent publications (Androutsopoulos et al., 1995; Chaves & Gerosa, 2021; Følstad & Skjuve, 2019; Hauswald et al., 2016).”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Androutsopoulos et al., 1995
Citations
Cites
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771
A relational model of data for large shared data banks
Codd, E. F. (1970) · Communications of the ACM · doi:10.1145/362384.362685 · Cited by 5,547
A relational model of data for large shared data banks
Codd, E. F. (1983) · Communications of the ACM · doi:10.1145/357980.358007 · Cited by 4,797
Principles of database and knowledge-base systems
(1989) · Choice Reviews Online · doi:10.5860/choice.26-5107 · Cited by 2,701
Generalized Phrase Structure Grammar.
Soames, S., Gazdar, G. J. M., Klein, E., Pullum, G. K., & Sag, I. A. (1989) · The Philosophical Review · doi:10.2307/2185122 · Cited by 1,829
Introduction to Montague Semantics
Dowty, D. R., Wall, R. E., & Peters, S. (1980) · Studies in linguistics and philosophy · doi:10.1007/978-94-009-9065-4 · Cited by 1,112
An introduction to unification-based approaches to grammar
Shieber, S. M. (1986) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 935
6. Generalized Phrase Structure Grammar
Steurs, F. (1991) · Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory · doi:10.1075/cilt.75.08ste · Cited by 758
Logic Programming and Databases
Ceri, S., Gottlob, G., & Tanca, L. (1990) · Surveys in computer science · doi:10.1007/978-3-642-83952-8 · Cited by 750
Flagged first, then by citations
5 of the 101 references couldn’t be matched to a known paper.
Research works printed but not linked: 35
- Woods W.A. , Kaplan R.M. , and Webber B.N. , (1972) The Lunar Sciences natural language information system: Final Report. BBN Report 2378, Bolt Beranek and Newman Inc., Cambridge, Massachusetts.
- Weischedel, 1989, Proceedings of the 27th Annual Meeting of ACL, 193
- Thompson, 1969, Proceedings of the 24th ACM National Converence, 399
- Tennant, 1983, Proceedings of the 21st Annual Meeting of ACL, 151
- Scha, 1977, SIGART Newsletter
- 1991, Natural Language Markets
- Bobrow, 1990, Processings of the 28th Annual Meeting of ACL, 227
- KBMS – Knowledge Base Management System
- Carbonell, 1983, Proceedings of the 21st Annual Meeting of ACL, 164
- Ballard, 1984, Processings of the 22nd Annual Meeting of ACL, 52
- Ballard, 1986, Proceedings of the 24th Annual Meeting of ACL, 20
- Androutsopoulos I , (1993) Interfacing a natural language front-end to a relational database. MSc thesis. Technical paper 11, Department of Artificial Intelligence, University of Edinburgh.
- Tennant, 1983, Proceedings of CHI'83, Conference on Human Factors in Computer Systems
- Lopes, 1984, Proeedings of the 22nd Annual Meetings of ACL
- Androutsopoulos, 1993, Proceedings of the 6th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Edinburgh, U.K, 327
- Woods, 1968, Proceedings of the Fall Joint Computer Conference, 457
- Sentance S. , (1989) Improved responses from an English language front end. M.Sc. thesis, Dept. of Artificial Intelligence, University of Edinburgh.
- Alshawi, 1992, The Core Language Engine
- Harris, 1978, Processings of the ACM'78 Annual Conference
- INTELLECT – Natural Language System
- Codd, 1974, Data Base Management
- De Roeck, 1986, Proceedings of the 11th International Conference on Computational Linguistics
- Diaper, 1986, People and Computers: Designing for Usability - Proceedings of the Second Conference of the British Computer Society, Human Computer Interaction Specialist Group, University of York, 263
- Whittemore, 1990, Proceedings of the 28th Annual Meting of ACL, 23
- Gerlach, 1989, Proceedings of the 4th Conference of the European Chapter of ACL, 27
- Giarratano, 1989, Expert Systems: Priniciples and Programming
- Hafner, 1984, Proceedings of the 22nd Annual Meeting of ACL, 57
- Hafner, 1985, Proceedings of the 23rd Annual Meeting of ACL, 1
- Bobrow, 1978, Research in Natural Language Understanding, BBN Report 3878
- Hendrix, 1982, Natural language interface (panel), Computational Linguistics, 8, 55
- Kaplan, 1983, Computational Models of Discourse, 167
- Martin, 1986, Readings in Natural Language Processing, 585
- Thompson, 1975, Advances in Computers, 109
- Lowden, 1986, REMIT: A natural language paraphraser for relational query expressions, ICL Technical Journal, 5, 32
- De Roeck, 1990, Proceedings of InfoJapan '90
Cited by
Sorted by citations
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Ramadijanti, N., Rahmadani, R., Muliawati, T. H., & Edelani, R. (2026) · International Electronics Symposium (IES) · doi:10.1109/ies71033.2026.11702596
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Lin, L. G., Shen, Y., Bao, L., Wu, R., & Liu, Y. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832269
Velagala, G. R. (2026) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic70896.2026.11716968
Yang, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02147-6
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Metadata
- Authors
- Androutsopoulos, I., Ritchie, G. D., & Thanisch, P.
- Affiliations
- University of Edinburgh (GB)
- ORCID
- Androutsopoulos, I.
- Venue
- Natural Language Engineering
- Publisher
- Cambridge University Press (CUP)
- Published
- 1995-03
- Volume
- 1
- Issue
- 1
- Pages
- 29-81
- ISSN
- 1351-3249 (ISSN-L)
- 1469-8110
- OpenAlex
- W2145618437
- Cited by
- 768
- References
- 101
- Citations by year
- 2026: 27 · 2025: 27 · 2024: 17 · 2023: 22 · 2022: 16 · 2021: 41 · 2020: 49 · 2019: 39 · 2018: 31 · 2017: 32 · 2016: 26 · 2015: 25 · 2014: 29 · 2013: 30 · 2012: 31
- FWCI
- 10.93
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Natural Language Engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- State Scholarships Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Keywords
- natural language interfaces to databases, graphical user interface, linguistic challenges, database architecture, portability, menu-based interface, reasoning skills, database updates, multimodal interfaces
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