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Journal of Logic and Computation Advance Access published online on December 13, 2007
Journal of Logic and Computation, doi:10.1093/logcom/exm067
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© The Author 2007. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Original papers

Linking Semantic and Knowledge Representations in a Multi-Domain Dialogue System

Myroslava O. Dzikovska

Human Communication Research Centre, 2 Buccleuch Place, Edinburgh EH8 9LW, UK. E-mail: m.dzikovska{at}ed.ac.uk

James F. Allen and Mary D. Swift

Department of Computer Science, University of Rochester, Rochester, NY 14526, USA. E-mail: james{at}cs.rochester.edu, swift{at}cs.rochester.edu

We describe a two-layer architecture for supporting semantic interpretation and domain reasoning in dialogue systems. Building system that supports both semantic interpretation and domain reasoning in a transparent and well-integrated manner is an unresolved problem because of the diverging requirements of the semantic representations used in contextual interpretation versus the knowledge representations used in domain reasoning. We propose an architecture that provides both portability and efficiency in natural language interpretation by maintaining separate semantic and domain knowledge representations, and integrating them via an ontology mapping procedure. The ontology mapping is used to obtain representations of utterances in a form most suitable for domain reasoners and to automatically specialize the lexicon. The use of a linguistically motivated parser for producing semantic representations for complex natural language sentences facilitates building portable semantic interpretation components as well as connections with domain reasoners. Two evaluations demonstrate the effectiveness of our approach: we show that a small number of mapping rules are sufficient for customizing the generic semantic representation to a new domain, and that our automatic lexicon specialization technique improves parser speed and accuracy.



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