  Ontology engineering (OE) in large projects poses a number of challenges
arising from the heterogeneous backgrounds of the various stakeholders, domain
experts, and their complex interactions with ontology designers. This
multi-party interaction often creates systematic ambiguities and biases from
the elicitation of ontology requirements, which directly affect the design,
evaluation and may jeopardise the target reuse. Meanwhile, current OE
methodologies strongly rely on manual activities (e.g., interviews, discussion
pages). After collecting evidence on the most crucial OE activities, we
introduce \textbf{OntoChat}, a framework for conversational ontology
engineering that supports requirement elicitation, analysis, and testing. By
interacting with a conversational agent, users can steer the creation of user
stories and the extraction of competency questions, while receiving
computational support to analyse the overall requirements and test early
versions of the resulting ontologies. We evaluate OntoChat by replicating the
engineering of the Music Meta Ontology, and collecting preliminary metrics on
the effectiveness of each component from users. We release all code at
https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.
