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：开源信息抽取（OpenIE）是一项关键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，不受关系类型或领域限制。本综述论文系统梳理了2007年至2024年间OpenIE技术的发展历程，强调了以往综述中缺失的时间脉络视角。论文考察了OpenIE任务设置的演进过程，以契合最新技术发展。将OpenIE方法划分为基于规则、神经网络和预训练大语言模型三类，并按照时间框架逐一探讨。此外，重点介绍了当前使用的常见数据集与评估指标。基于上述全面回顾，论文从数据集、信息源、输出格式、方法论及评估指标五个维度，展望了未来潜在研究方向。