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Time, tense and aspect in natural language database interfaces

Published online by Cambridge University Press:  01 September 1998

ION ANDROUTSOPOULOS
Affiliation:
Language Technology Group, Microsoft Research Institute, Macquarie University, Sydney NSW 2109, Australia; e-mail: ion@mri.mq.edu.au
GRAEME RITCHIE
Affiliation:
Department of Artificial Intelligence, University of Edinburgh, 80 South Bridge, Edinburgh EH1 1HN, Scotland; e-mail: G.D.Ritchie@ed.ac.uk
PETER THANISCH
Affiliation:
Department of Computer Science, University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3JZ, Scotland; e-mail: pt@dcs.ed.ac.uk

Abstract

Most existing Natural Language Database Interfaces (NLDB) were designed to be used with database systems that provide very limited facilities for manipulating time-dependent data, and they do not support adequately temporal linguistic mechanisms (verb tenses, temporal adverbials, temporal subordinate clauses, etc.). The database community is becoming increasingly interested in temporal database systems, which are intended to store and manipulate in a principled manner information not only about the present, but also about the past and future. When interfacing to temporal databases, supporting temporal linguistic mechanisms becomes crucial.

We present a framework for constructing Natural Language Interfaces for Temporal Databases (NLTDB), which draws on research in tense and aspect theories, temporal logics and temporal databases. The framework consists of a temporal intermediate representation language, called TOP, an HPSG grammar that maps a wide range of questions involving temporal mechanisms to appropriate TOP expressions, and a provably correct method for translating from TOP to TSQL2, TSQL2 being a recently proposed temporal extension of the SQL database language. This framework was employed to implement a prototype NLTDB.

Type
Research Article
Copyright
© 1998 Cambridge University Press

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Footnotes

This paper reports on work that was carried out while the first author was at the Department of Artificial Intelligence, University of Edinburgh, supported by the Greek State Scholarships Foundation.