Competency Questions (CQs) for an ontology and similar artefacts aim to provide insights into the contents of an ontology and to demarcate its scope. The absence of a controlled natural language, tooling and automation to support the authoring of CQs has hampered their effective use in ontology development and evaluation. The few question templates that exists are based on informal analyses of a small number of CQs and have limited coverage of question types and sentence constructions. We aim to fill this gap by proposing a template-based CNL to author CQs, called CLaRO. For its design, we exploited a new 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 additional CNL model and XML serialisation. The CNL was evaluated with a subset of questions from the original dataset and with two sets of newly sourced CQs. The coverage of CLaRO, with its 93 main templates and 41 linguistic variants, is about 90 for unseen questions. CLaRO has the potential to facilitate streamlining formalising ontology content requirements and, given that about one third of the competency questions in the test sets turned out to be invalid questions, assist in writing good questions.
In the context of the development of a virtual tutor to support distance learning courses, this paper presents an approach to solve the problem of automatically answering questions posed by students in a natural language (Portuguese). Our approach is based on three main pillars: an ontology, a conversion process, and a querying process. First, the ontology, was built to model the knowledge regarding all aspects of the course; second, we defined a way of converting a natural language question to a SPARQL query; finally, the SPARQL query is executed and the result extracted from the ontology. Focusing on the second pillar mentioned above (the conversion of a NL question), we explain the whole process and present the results regarding a set of preliminary experiments.