Explanatory Dialogues with Active Learning for Rule-based Expertise

Contemporary language models have enhanced the interaction capabilities of AI with users. The improved understanding and processing abilities of AI with respect to the provided data, thanks to these language models, have simplified related knowledge engineering tasks. In this research, we embed LLMs in computational agents to reinforce the interaction between the system and expert users to improve knowledge engineering processes. By combining explanatory dialogue and active learning into knowledge engineering pipelines, we provide a framework that can help experts validate rule-based expertise in a specific domain. This validated expertise can be represented in RuleML format and is available to support knowledge-driven AI applications in domain-specific tasks. Our initial test indicates that such an integration is feasible and improves the overall usability of knowledge engineering processes, using curriculum development scenarios from DIGITAL4Business, a four-year EU-funded project to deliver a new European Master’s programme on the practical application of advanced digital skills within European SMEs and companies.