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Competency Questions (CQs) for an ontology and similar artefacts aim toprovide insights into the contents of an ontology and to demarcate its scope.The absence of a controlled natural language, tooling and automation to supportthe authoring of CQs has hampered their effective use in ontology developmentand evaluation. The few question templates that exists are based on informalanalyses of a small number of CQs and have limited coverage of question typesand sentence constructions. We aim to fill this gap by proposing atemplate-based CNL to author CQs, called CLaRO. For its design, we exploited anew dataset of 234 CQs that had been processed automatically into 106 patterns,which we analysed and used to design a template-based CNL, with an additionalCNL model and XML serialisation. The CNL was evaluated with a subset ofquestions from the original dataset and with two sets of newly sourced CQs. Thecoverage of CLaRO, with its 93 main templates and 41 linguistic variants, isabout 90% for unseen questions. CLaRO has the potential to facilitatestreamlining formalising ontology content requirements and, given that aboutone third of the competency questions in the test sets turned out to be invalidquestions, assist in writing good questions.

CLaRO: a Data-driven CNL for Specifying Competency Questions