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综述。通过使用自动化框架收集和标注文献，我们分析了2019年至2024年间发表的34篇综述、64个数据集和104个模型。该综述重点探讨了方法学进展、基准资源以及语义网技术的融合。通过整合多维度结果，本研究识别了当前趋势、局限性与开放挑战，为研究人员和实践者理解关系抽取领域的发展脉络与未来方向提供了全面的参考依据。