Automatic verbalisation of SNOMED classes using OntoVerbal

Authors:
Shao Fen Liang;Robert Stevens;Donia Scott;Alan Rector
Affiliations:
School of Computer Science, University of Manchester, Manchester, UK;School of Computer Science, University of Manchester, Manchester, UK;School of Informatics, University of Sussex, Brighton, UK;School of Computer Science, University of Manchester, Manchester, UK
Venue:
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Year:
2011

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Abstract

SNOMED is a large description logic based terminology for recording in electronic health records. Often, neither the labels nor the description logic definitions are easy for users to understand. Furthermore, information is increasingly being recorded not just using individual SNOMED concepts but also using complex expressions in the description logic ("post-coordinated" concepts). Such post-coordinated expressions are likely to be even more complex than other definitions, and therefore can have no pre-assigned labels. Automatic verbalisation will be useful both for understanding and quality assurance of SNOMED definitions, and for helping users to understand post-coordinated expressions. OntoVerbal is a system that presents a compositional terminology expressed in OWL as natural language. We describe the application of OntoVerbal to SNOMED-CT, whereby SNOMED classes are presented as textual paragraphs through the use of natural language generation technology.