Large Language Model Assisted Multi-Agent Dialogue for Ontology Alignment

Shiyao Zhang, Yuji Dong*, Yichuan Zhang, Terry R. Payne, Jie Zhang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Ontology alignment is critical in cross-domain integration; however, it typically necessitates the involvement of a human domain-expert, which can make the task costly. Although a variety of machine-learning approaches have been proposed that can simplify this task by learning the patterns from experts, such techniques are still susceptible to domain knowledge updates that could potentially change the patterns and lead to extra expert involvement. The use of Large Language Models (LLMs) has demonstrated a general cognitive ability, which has the potential to assist ontology alignment from the cognition level, thus obviating the need for costly expert involvement. However, the process by which the output of LLMs is generated can be opaque and thus the reliability and interpretability of such models is not always predictable. This paper proposes a dialogue model, in which multiple agents negotiate the correspondence between two knowledge sets with the support from an LLM. We demonstrate that this approach not only reduces the need for the involvement of a domain expert for ontology alignment, but that the results are interpretable despite the use of LLMs.

Original languageEnglish
Pages (from-to)2594-2596
Number of pages3
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2024-May
Publication statusPublished - 2024
Event23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024 - Auckland, New Zealand
Duration: 6 May 202410 May 2024

Keywords

  • Dialogue
  • Large Language Model
  • Multi-Agent System
  • Negotiation
  • Ontology Alignment

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