Automating the Generation of Competency Questions for Ontologies with AgOCQs

Antia, Mary-Jane and Keet, C. Maria (2023) Automating the Generation of Competency Questions for Ontologies with AgOCQs, Proceedings of 5th Iberoamerican conference on Knowledge Graphs and Semantic Web (KGSWC'23), Springer Nature Switzerland.

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Abstract

Competency Questions (CQs) are natural language questions drawn from a chosen subject domain and are intended for use in ontology engineering processes. Authoring good quality and answerable CQs has been shown to be difficult and time-consuming, due to, among others, manual authoring, relevance, answerability, and re-usability. As a result, few ontologies are accompanied by few CQs and their uptake among ontology developers remains low. We aim to address the challenges with manual CQ authoring through automating CQ generation. This novel process, called AgOCQs, leverages a combination of Natural Language Processing (NLP) techniques, corpus and transfer learning methods, and an existing controlled natural language for CQs. AgOCQs was applied to CQ generation from a corpus of Covid-19 research articles, and a selection of the generated questions was evaluated in a survey. 70% of the CQs were judged as being grammatically correct by at least 70% of the participants. For 12 of the 20 evaluated CQs, the ontology expert participants deemed the CQs to be answerable by an ontology at a range of 50$$\backslash%$$-85$$\backslash%$$across the CQs, with the remainder uncertain. This same group of ontology experts found the CQs to be relevant between 70$$\backslash%$$-93$$\backslash%$$across the 12 CQs. Finally, 73$$\backslash%$$of the users group and 69$$\backslash%$$of the ontology experts judged all the CQs to provide clear domain coverage. These findings are promising for the automation of CQs authoring, which should reduce development time for ontology developers.

Item Type:	Conference paper
Subjects:	Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Ontology engineering
Date Deposited:	10 Nov 2023 14:46
Last Modified:	10 Nov 2023 14:46
URI:	https://pubs.cs.uct.ac.za/id/eprint/1614

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