Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text, unrestricted by relation type or domain. This survey paper provides an overview of OpenIE technologies spanning from 2007 to 2024, emphasizing a chronological perspective absent in prior surveys. It examines the evolution of task settings in OpenIE to align with the advances in recent technologies. The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. Additionally, it highlights prevalent datasets and evaluation metrics currently in use. Building on this extensive review, the paper outlines potential future directions in terms of datasets, information sources, output formats, methodologies, and evaluation metrics.

翻译：开放信息抽取（OpenIE）是一项重要的自然语言处理任务，旨在从非结构化文本中提取结构化信息，且不受关系类型或领域限制。本综述论文概述了2007年至2024年间的OpenIE技术，重点强调了此前综述中缺失的时序视角。论文考察了OpenIE任务设置随近期技术进步的演进过程，将OpenIE方法分为基于规则、基于神经网络和基于预训练大型语言模型三类，并在时间框架内逐一讨论。此外，论文还重点介绍了当前常用的数据集和评估指标。基于全面综述，论文从数据集、信息源、输出格式、方法论和评估指标五个维度展望了未来潜在研究方向。