Using graph transformation algorithms to generate natural language equivalents of icons expressing medical concepts
This work addresses the need for better communication tools in medical contexts by providing a method to translate visual icons into natural language, though it appears incremental as it applies an existing informatics method to a specific domain.
The paper tackled the problem of generating natural language equivalents from medical icons by using graph transformation algorithms to map OWL-DL ontology concepts into semantic structures for natural language generation, aiming to enhance the usability of a graphical medical language.
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.