This article presents a systematic review of relation extraction (RE) research since the advent of Transformer-based models. Using an automated framework to collect and annotate publications, we analyze 34 surveys, 64 datasets, and 104 models published between 2019 and 2024. The review highlights methodological advances, benchmark resources, and the integration of semantic web technologies. By consolidating results across multiple dimensions, the study identifies current trends, limitations, and open challenges, offering researchers and practitioners a comprehensive reference for understanding the evolution and future directions of RE.

翻译：本文对Transformer模型出现以来的关系抽取研究进行了系统性综述。通过采用自动化框架收集并标注相关文献，我们分析了2019年至2024年间发表的34篇综述、64个数据集及104个模型。本综述重点阐述了方法学进展、基准资源以及语义网技术的融合应用。通过整合多维度研究成果，本研究揭示了当前趋势、现有局限与开放挑战，为研究人员与实践者理解关系抽取领域的发展脉络及未来方向提供了全面的参考依据。