An Experiment in Retrofitting Competency Questions for Existing Ontologies
This addresses a gap in ontology engineering by enabling retrofitting of CQs for improved reuse and testing, though it is incremental as it builds on existing AI methods.
The authors tackled the problem of missing Competency Questions (CQs) in published ontologies by proposing RETROFIT-CQs, a method using Generative AI to extract candidate CQs from existing ontologies, and applied it to several ontologies to demonstrate feasibility.
Competency Questions (CQs) are a form of ontology functional requirements expressed as natural language questions. Inspecting CQs together with the axioms in an ontology provides critical insights into the intended scope and applicability of the ontology. CQs also underpin a number of tasks in the development of ontologies e.g. ontology reuse, ontology testing, requirement specification, and the definition of patterns that implement such requirements. Although CQs are integral to the majority of ontology engineering methodologies, the practice of publishing CQs alongside the ontological artefacts is not widely observed by the community. In this context, we present an experiment in retrofitting CQs from existing ontologies. We propose RETROFIT-CQs, a method to extract candidate CQs directly from ontologies using Generative AI. In the paper we present the pipeline that facilitates the extraction of CQs by leveraging Large Language Models (LLMs) and we discuss its application to a number of existing ontologies.