Work

Explanatory Dialogues with Active Learning for Rule-based Expertise

Year: 2024

Type: book-chapter

Abstract: 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.

Source: TRAP@NCI (National College of Ireland)

Authors: Yao, Yao, González-Vélez, Horacio, Croitoru, Madalina

Institutions: National College of Ireland, Université de Montpellier, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier

Language: English

Topic: Semantic Web and Ontologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funders: European Commission, Enterprise Ireland, Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, Eurostars

Award: G5750848692