Title: Formalisation and classification of grammar and template-mediated techniques to model and ontology verbalisation
Authors: Zola Mahlaza; C. Maria Keet
Addresses: Department of Computer Science, University of Cape Town, Rondebosch, Cape Town, South Africa ' Department of Computer Science, University of Cape Town, Rondebosch, Cape Town, South Africa
Abstract: Computational tools that translate modelling languages to a restricted natural language can improve end-user involvement in modelling. Templates are a popular approach for such a translation and are often paired with computational grammar rules to support grammatical complexity to obtain better quality sentences. There is no explicit specification of the relations used for the pairing of templates with grammar rules, so it is challenging to compare the latter templates' suitability for less-resourced languages, where grammar reuse is vital in reducing development effort. In order to enable such comparisons, we devise a model of pairing templates and rules, and assess its applicability by considering 54 existing systems for classification, and 16 of them in detail. Our classification shows that most grammar-infused template systems support detachable grammar rules and half of them introduce syntax trees for multilingualism or error checking. Furthermore, out of the 16 considered grammar-infused template systems, most do not currently support any of form of aggregation (63%) or the embedding of verb conjugation rules (81%); hence, if such features would be required, then they would need to be implemented from the ground up.
Keywords: ontology verbalisation; model verbalisation; natural language generation; template classification; grammar-infused templates.
International Journal of Metadata, Semantics and Ontologies, 2020 Vol.14 No.3, pp.249 - 262
Received: 08 Jul 2020
Accepted: 10 Dec 2020
Published online: 26 Jan 2021 *