# Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications

arXiv: https://arxiv.org/abs/2310.11644 · HTML: https://mlsanity.com/paper/2310.11644/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [MuaLLM: A Multimodal Large Language Model Agent for Circuit Design Assistance with Hybrid Contextual Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.08137/) (2025)
- [Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model](https://mlsanity.com/paper/2606.17950/) (2026)

## Abstract (arXiv)

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.