OWLSIZ: An isiZulu CNL for structured knowledge validation

Zola Mahlaza, C. Maria Keet


Abstract
In iterative knowledge elicitation, engineers are expected to be directly involved in validating the already captured knowledge and obtaining new knowledge increments, thus making the process time consuming. Languages such as English have controlled natural languages than can be repurposed to generate natural language questions from an ontology in order to allow a domain expert to independently validate the contents of an ontology without understanding a ontology authoring language such as OWL. IsiZulu, South Africa’s main L1 language by number speakers, does not have such a resource, hence, it is not possible to build a verbaliser to generate such questions. Therefore, we propose an isiZulu controlled natural language, called OWL Simplified isiZulu (OWLSIZ), for producing grammatical and fluent questions from an ontology. Human evaluation of the generated questions showed that participants’ judgements agree that most (83%) questions are positive for grammaticality or understandability.
Anthology ID:
2020.webnlg-1.2
Volume:
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
Month:
12
Year:
2020
Address:
Dublin, Ireland (Virtual)
Editors:
Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, Anastasia Shimorina
Venue:
WebNLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
15–25
Language:
URL:
https://aclanthology.org/2020.webnlg-1.2
DOI:
Bibkey:
Cite (ACL):
Zola Mahlaza and C. Maria Keet. 2020. OWLSIZ: An isiZulu CNL for structured knowledge validation. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 15–25, Dublin, Ireland (Virtual). Association for Computational Linguistics.
Cite (Informal):
OWLSIZ: An isiZulu CNL for structured knowledge validation (Mahlaza & Keet, WebNLG 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.webnlg-1.2.pdf