  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.
