Using graph transformation algorithms to generate natural language equivalents of icons expressing medical concepts

TL;DR

This paper presents a graph transformation approach to convert OWL-DL medical concepts into deep semantic structures for natural language generation, enhancing communication of medical information through icons.

Contribution

It introduces a novel application of graph transformation algorithms to prepare ontology concepts for natural language generation in medical iconography.

Findings

Transforms OWL-DL concepts into semantic graphs

Facilitates natural language generation from ontologies

Potentially applicable to other ontology-to-language mappings

Abstract

A graphical language addresses the need to communicate medical information in a synthetic way. Medical concepts are expressed by icons conveying fast visual information about patients' current state or about the known effects of drugs. In order to increase the visual language's acceptance and usability, a natural language generation interface is currently developed. In this context, this paper describes the use of an informatics method ---graph transformation--- to prepare data consisting of concepts in an OWL-DL ontology for use in a natural language generation component. The OWL concept may be considered as a star-shaped graph with a central node. The method transforms it into a graph representing the deep semantic structure of a natural language phrase. This work may be of future use in other contexts where ontology concepts have to be mapped to half-formalized natural language expressions.