Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Awesome papers about unifying LLMs and KGs
| Date | Stars |
|---|---|
| 2026-07-31 | 2613 |
| 2026-08-02 | 2613 |
| 2026-08-03 | 2613 |
| 2026-08-12 | 2614 |
| 2026-08-18 | 2613 |
| 2026-08-28 | 2614 |
| 2026-09-06 | 2613 |
| 2026-09-07 | 2614 |
| 2026-09-16 | 2615 |
| 2026-09-20 | 2615 |
Today
— stars today
This week
+1 stars this week
This month
+2 stars this month
Momentum
0.0
growth rate 0.04%/day
# Awesome-LLM-KG
[](https://github.com/RManLuo/Awesome-LLM-KG)
[](https://opensource.org/licenses/MIT)
A collection of papers and resources about unifying large language models (LLMs) and knowledge graphs (KGs).
Large language models (LLMs) have achieved remarkable success and generalizability in various applications. However, they often fall short of capturing and accessing factual knowledge. Knowledge graphs (KGs) are structured data models that explicitly store rich factual knowledge. Nevertheless, KGs are hard to construct and existing methods in KGs are inadequate in handling the incomplete and dynamically changing nature of real-world KGs. Therefore, it is natural to unify LLMs and KGs together and simultaneously leverage their advantages.
<img src="figs/PLM_vs_KG.png" width = "600" />
## News
🔭 This project is under development. You can hit the **STAR** and **WATCH** to follow the updates.
* We are happy to release the first **graph foundation model**-powered RAG pipeline (GFM-RAG) that combines the power of GNNs with LLMs to enhance reasoning. [Paper](https://www.arxiv.org/abs/2502.01113) and [Code](https://github.com/RManLuo/gfm-rag).
* Our latest work on KG + LLM reasoning: [Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models](https://arxiv.org/abs/2410.13080) has been accepted by ICML 2025.
* Our LLM for temporal KG reasoning work: [Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2405.14170) has been accepted by NeurIPS 2024!
* Our KG for analyzing LLM reasoning paper: [Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning
with Knowledge Graphs](https://arxiv.org/abs/2402.11199) has been accepted by ACL 2024.
* Our [roadmap paper](https://arxiv.org/abs/2306.08302) has been accepted by TKDE.
* Our KG for LLM probing paper: [Systematic Assessment of Factual Knowledge in Large Language Models](https://arxiv.org/abs/2310.11638) has been accepted by EMNLP 2023.
* Our KG + LLM reasoning paper: [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://arxiv.org/abs/2310.01061) has been accepted by ICLR 2024.
* Our LLM for KG reasoning paper: [ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning](https://arxiv.org/abs/2309.01538) is now public.
* Our roadmap paper: [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://arxiv.org/abs/2306.08302) is now public.
## Overview
In this repository, we collect recent advances in unifying LLMs and KGs. We present a roadmap that summarizes three general frameworks: *1) KG-enhanced LLMs*, *2) LLMs-augmented KGs*, and *3) Synergized LLMs + KGs*.
<img src="figs/roadmap.png" width = "800" />
We also illustrate the involved techniques and applications.
<img src="figs/Unifying.png" width = "600" />
We hope this repository can help researchers and practitioners to get a better understanding of this emerging field.
If this repository is helpful for you, plase help us by citing this paper:
```bash
@article{llm_kg,
title={Unifying Large Language Models and Knowledge Graphs: A Roadmap},
author={Pan, Shirui and Luo, Linhao and Wang, Yufei and Chen, Chen and Wang, Jiapu and Wu, Xindong},
journal={IEEE Transactions on Knowledge and Data Engineering (TKDE)},
year={2024}
}
```
## Table of Contents
- [Awesome-LLM-KG](#awesome-llm-kg)
- [News](#news)
- [Overview](#overview)
- [Table of Contents](#table-of-contents)
- [Related Surveys](#related-surveys)
- [KG-enhanced LLMs](#kg-enhanced-Excerpt of 20,223 characters
Read on GitHub
44
4
2
Zhuo Chen · Zhejiang University · China
2
2
Ishaan Singh Rawal
1
1
1
1
Eric
1
1
Shengyuan Chen
1
yueliu1999 · National University of Singapore · Singapore
1
Jinheon Baek
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4a0223c8e2eb194a, topic:llm, topic:language-model
matched fp:4a0223c8e2eb194a, topic:knowledge-graph
matched fp:4a0223c8e2eb194a, topic:chatgpt