# Research Trends for the Interplay between Large Language Models and Knowledge Graphs

arXiv: https://arxiv.org/abs/2406.08223 · HTML: https://mlsanity.com/paper/2406.08223/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 97 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 30 of 55)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://mlsanity.com/paper/2310.01061/) (2023)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering](https://mlsanity.com/paper/2306.04136/) (2023)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models](https://mlsanity.com/paper/2203.02167/) (2022)
- [A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions](https://mlsanity.com/paper/2105.11644/) (2021)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [A Survey of Knowledge Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2211.05994/) (2022)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://mlsanity.com/paper/2305.02105/) (2023)
- [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2106.10502/) (2021)
- [Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling](https://mlsanity.com/paper/2306.11489/) (2023)

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

- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [Combining Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2407.06564/) (2024)
- [Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study](https://mlsanity.com/paper/2506.19773/) (2025)
- [LLM+KG@VLDB'24 Workshop Summary](https://mlsanity.com/paper/2410.01978/) (2024)
- [FastRAG: Retrieval Augmented Generation for Semi-structured Data](https://mlsanity.com/paper/2411.13773/) (2024)
- [LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics](https://mlsanity.com/paper/2503.07993/) (2025)
- [Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging](https://mlsanity.com/paper/2503.13499/) (2025)
- [Trusted Knowledge Extraction for Operations and Maintenance Intelligence](https://mlsanity.com/paper/2507.22935/) (2025)
- [Automated Archival Descriptions with Federated Intelligence of LLMs](https://mlsanity.com/paper/2504.05711/) (2025)
- [Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2510.20691/) (2025)
- [Matching Game Preferences Through Dialogical Large Language Models: A Perspective](https://mlsanity.com/paper/2507.20000/) (2025)
- [Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation](https://mlsanity.com/paper/2604.03496/) (2026)
- [MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA](https://mlsanity.com/paper/2607.14561/) (2026)
- [From Queries to Narratives: Cultural Heritage Data Stories for Knowledge Graph Exploration and Quality Assessment](https://mlsanity.com/paper/2609.11403/) (2026)
- [Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing](https://mlsanity.com/paper/2610.08095/) (2026)

## Abstract (arXiv)

This survey investigates the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in understanding, reasoning, and language processing. It aims to address gaps in current research by exploring areas such as KG Question Answering, ontology generation, KG validation, and the enhancement of KG accuracy and consistency through LLMs. The paper further examines the roles of LLMs in generating descriptive texts and natural language queries for KGs. Through a structured analysis that includes categorizing LLM-KG interactions, examining methodologies, and investigating collaborative uses and potential biases, this study seeks to provide new insights into the combined potential of LLMs and KGs. It highlights the importance of their interaction for improving AI applications and outlines future research directions.