Knowledge Base-enhanced Multilingual Relation Extraction with Large Language Models

Relation Extraction (RE) is an essential task that involves comprehending relational facts between entities from natural language texts. However, existing research in RE, particularly those based on large language models (LLMs), is proven to fall short in the task due to their context unawareness (lack of fine-grained understanding), schema misalignment (misaligned with human-defined schema), and world knowledge ignorance (relying solely on internal parametric knowledge). In this paper, we propose a novel framework to address the aforementioned challenges. The framework consists of two stages, including 1) entity linking and 2) relation inference, by fully leveraging the efficacy of external knowledge bases (KBs) and LLMs in this task. We conduct extensive experiments in a multilingual setting and achieve state-of-the-art performance on the experimented datasets. The LLMs with external knowledge can typically outperform those without knowledge by a significant margin, indicating the effectiveness of our proposed framework.