# More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction

arXiv: https://arxiv.org/abs/2004.03186 · HTML: https://mlsanity.com/paper/2004.03186/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 30 of 52)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
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- [Structured information extraction from complex scientific text with fine-tuned large language models](https://mlsanity.com/paper/2212.05238/) (2022)
- [KG-RAG: Bridging the Gap Between Knowledge and Creativity](https://mlsanity.com/paper/2405.12035/) (2024)
- [Curriculum-Meta Learning for Order-Robust Continual Relation Extraction](https://mlsanity.com/paper/2101.01926/) (2021)
- [Revisiting Large Language Models as Zero-shot Relation Extractors](https://mlsanity.com/paper/2310.05028/) (2023)
- [A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2101.10213/) (2021)
- [ADELIE: Aligning Large Language Models on Information Extraction](https://mlsanity.com/paper/2405.05008/) (2024)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Entity Concept-enhanced Few-shot Relation Extraction](https://mlsanity.com/paper/2106.02401/) (2021)
- [Multi-hop Evidence Retrieval for Cross-document Relation Extraction](https://mlsanity.com/paper/2212.10786/) (2022)
- [RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2306.04954/) (2023)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.09543/) (2021)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Deep Neural Network Based Relation Extraction: An Overview](https://mlsanity.com/paper/2101.01907/) (2021)
- [Towards Relation Extraction From Speech](https://mlsanity.com/paper/2210.08759/) (2022)
- [Cross-lingual Lifelong Learning](https://mlsanity.com/paper/2205.11152/) (2022)
- [Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2402.11142/) (2024)
- [Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors](https://mlsanity.com/paper/2104.08225/) (2021)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2110.07415/) (2021)
- [Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset](https://mlsanity.com/paper/2209.13464/) (2022)
- [Predicting Document Coverage for Relation Extraction](https://mlsanity.com/paper/2111.13611/) (2021)
- [PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction](https://mlsanity.com/paper/2304.01209/) (2023)
- [Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations](https://mlsanity.com/paper/2404.05415/) (2024)
- [Schema as Parameterized Tools for Universal Information Extraction](https://mlsanity.com/paper/2506.01276/) (2025)
- [Making Document-Level Information Extraction Right for the Right Reasons](https://mlsanity.com/paper/2110.07686/) (2021)
- [MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction](https://mlsanity.com/paper/2204.04779/) (2022)

## Abstract (arXiv)

Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require "more" from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.