With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words. For this purpose, there have been many studies recently in Open Information Extraction (OIE). OIE improves upon relation extraction techniques by analyzing relations across different domains and avoids requiring hand-labeling pre-specified relations in sentences. This paper surveys recent approaches of OIE and its applications on Knowledge Graph (KG), text summarization, and Question Answering (QA). Moreover, the paper describes OIE basis methods in relation extraction. It briefly discusses the main approaches and the pros and cons of each method. Finally, it gives an overview about challenges, open issues, and future work opportunities for OIE, relation extraction, and OIE applications.

翻译：随着在线与离线文本数据的海量增长，提取短语间关系并以简短语言概括每篇文档核心内容的需求日益迫切。为此，近年来开放信息抽取（OIE）领域涌现了大量研究。OIE通过分析跨领域关系改进关系抽取技术，并避免了在句子中人工标注预定义关系的需求。本文综述了OIE的最新方法及其在知识图谱（KG）、文本摘要和问答（QA）中的应用。此外，本文阐述了OIE在关系抽取中的基础方法，简要讨论了各类主要方法及其优缺点。最后，文章概述了OIE、关系抽取及其应用面临的挑战、开放问题及未来研究方向。