# Knowledge Graph Generation From Text

arXiv: https://arxiv.org/abs/2211.10511 · HTML: https://mlsanity.com/paper/2211.10511/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [Language Models are Open Knowledge Graphs](https://mlsanity.com/paper/2010.11967/) (2020)
- [Commonsense Knowledge Base Completion with Structural and Semantic Context](https://mlsanity.com/paper/1910.02915/) (2019)
- [CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training](https://mlsanity.com/paper/2006.04702/) (2020)
- [ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models](https://mlsanity.com/paper/2108.12472/) (2021)
- [DualTKB: A Dual Learning Bridge between Text and Knowledge Base](https://mlsanity.com/paper/2010.14660/) (2020)

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation](https://mlsanity.com/paper/2404.01532/) (2024)
- [Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss](https://mlsanity.com/paper/2402.12269/) (2024)
- [Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study](https://mlsanity.com/paper/2506.19773/) (2025)
- [Cascading Large Language Models for Salient Event Graph Generation](https://mlsanity.com/paper/2406.18449/) (2024)
- [Evaluating Multi-Hop Reasoning in Large Language Models: A Chemistry-Centric Case Study](https://mlsanity.com/paper/2504.16414/) (2025)
- [AIDE: Attribute-Guided MultI-Hop Data Expansion for Data Scarcity in Task-Specific Fine-tuning](https://mlsanity.com/paper/2412.06136/) (2024)
- [MedG-KRP: Medical Graph Knowledge Representation Probing](https://mlsanity.com/paper/2412.10982/) (2024)
- [Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languages](https://mlsanity.com/paper/2302.04790/) (2023)
- [Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements](https://mlsanity.com/paper/2311.00444/) (2023)
- [RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19 Assessment in Primary Care](https://mlsanity.com/paper/2306.17175/) (2023)
- [Evaluating and Improving Graph to Text Generation with Large Language Models](https://mlsanity.com/paper/2501.14497/) (2025)
- [Systematic Evaluation of Knowledge Graph Repair with Large Language Models](https://mlsanity.com/paper/2507.22419/) (2025)
- [Fine-Grained Graph Generation through Latent Mixture Scheduling](https://mlsanity.com/paper/2605.02780/) (2026)

## Abstract (arXiv)

In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages. The graph nodes are generated first using pretrained language model, followed by a simple edge construction head, enabling efficient KG extraction from the text. For each stage we consider several architectural choices that can be used depending on the available training resources. We evaluated the model on a recent WebNLG 2020 Challenge dataset, matching the state-of-the-art performance on text-to-RDF generation task, as well as on New York Times (NYT) and a large-scale TekGen datasets, showing strong overall performance, outperforming the existing baselines. We believe that the proposed system can serve as a viable KG construction alternative to the existing linearization or sampling-based graph generation approaches. Our code can be found at https://github.com/IBM/Grapher