Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting

TL;DR

This paper enhances ontology requirements engineering by integrating OntoChat with participatory prompting, enabling more effective user story creation and requirement elicitation through researcher-mediated LLM interactions.

Contribution

It introduces participatory prompting to improve user engagement with OntoChat, facilitating better requirement gathering in ontology engineering.

Findings

Participatory prompting improves user story quality.

Researchers developed effective prompt templates.

Enhanced requirement elicitation process demonstrated.

Abstract

Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects. Current OntoChat offers a framework for ORE that utilises large language models (LLMs) to streamline the process through four key functions: user story creation, competency question (CQ) extraction, CQ filtration and analysis, and ontology testing support. In OntoChat, users are expected to prompt the chatbot to generate user stories. However, preliminary evaluations revealed that they struggle to do this effectively. To address this issue, we experimented with a research method called participatory prompting, which involves researcher-mediated interactions to help users without deep knowledge of LLMs use the chatbot more effectively. This participatory prompting user study produces pre-defined prompt templates based on user queries, focusing on creating and refining personas, goals, scenarios, sample data, and data resources for user stories. These refined user stories will subsequently be converted into CQs.