# Exploring Large Language Models for Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2308.13916 · HTML: https://mlsanity.com/paper/2308.13916/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 26 Aug 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models](https://mlsanity.com/paper/2203.02167/) (2022)
- [Sequence-to-Sequence Knowledge Graph Completion and Question Answering](https://mlsanity.com/paper/2203.10321/) (2022)
- [KBQA: Learning Question Answering over QA Corpora and Knowledge Bases](https://mlsanity.com/paper/1903.02419/) (2019)
- [Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion](https://mlsanity.com/paper/2209.07299/) (2022)
- [TransG : A Generative Mixture Model for Knowledge Graph Embedding](https://mlsanity.com/paper/1509.05488/) (2015)
- [SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions](https://mlsanity.com/paper/1604.04835/) (2016)
- [Knowledge Graph Representation with Jointly Structural and Textual Encoding](https://mlsanity.com/paper/1611.08661/) (2016)
- [KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction](https://mlsanity.com/paper/2211.02744/) (2022)
- [DOLORES: Deep Contextualized Knowledge Graph Embeddings](https://mlsanity.com/paper/1811.00147/) (2018)
- [LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings](https://mlsanity.com/paper/2210.00305/) (2022)

## Built on by (most cited first, 30 of 43)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs](https://mlsanity.com/paper/2410.14052/) (2024)
- [GRAG: Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2405.16506/) (2024)
- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [Making Large Language Models Perform Better in Knowledge Graph Completion](https://mlsanity.com/paper/2310.06671/) (2023)
- [Graph Machine Learning in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2404.14928/) (2024)
- [TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs](https://mlsanity.com/paper/2502.19512/) (2025)
- [MKGL: Mastery of a Three-Word Language](https://mlsanity.com/paper/2410.07526/) (2024)
- [GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation](https://mlsanity.com/paper/2503.12600/) (2025)
- [KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge](https://mlsanity.com/paper/2405.16412/) (2024)
- [UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction](https://mlsanity.com/paper/2402.06861/) (2024)
- [Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2311.08732/) (2023)
- [Large Language Model Enhanced Knowledge Representation Learning: A Survey](https://mlsanity.com/paper/2407.00936/) (2024)
- [Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation](https://mlsanity.com/paper/2410.16597/) (2024)
- [KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction](https://mlsanity.com/paper/2404.15923/) (2024)
- [Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion](https://mlsanity.com/paper/2407.16127/) (2024)
- [A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models](https://mlsanity.com/paper/2402.10779/) (2024)
- [Embedding Ontologies via Incorporating Extensional and Intensional Knowledge](https://mlsanity.com/paper/2402.01677/) (2024)
- [A Self-matching Training Method with Annotation Embedding Models for Ontology Subsumption Prediction](https://mlsanity.com/paper/2402.16278/) (2024)
- [Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion](https://mlsanity.com/paper/2405.06696/) (2024)
- [Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis](https://mlsanity.com/paper/2509.07122/) (2025)
- [On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection](https://mlsanity.com/paper/2510.10456/) (2025)
- [Is Large Language Model Good at Triple Set Prediction? An Empirical Study](https://mlsanity.com/paper/2412.18443/) (2024)
- [KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models](https://mlsanity.com/paper/2501.02711/) (2025)
- [Enhancing Knowledge Graph Construction: Evaluating with Emphasis on Hallucination, Omission, and Graph Similarity Metrics](https://mlsanity.com/paper/2502.05239/) (2025)
- [Talking to GDELT Through Knowledge Graphs](https://mlsanity.com/paper/2503.07584/) (2025)
- [KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models](https://mlsanity.com/paper/2506.03576/) (2025)
- [KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answering](https://mlsanity.com/paper/2506.07037/) (2025)
- [Ontology-Enhanced Knowledge Graph Completion using Large Language Models](https://mlsanity.com/paper/2507.20643/) (2025)
- [Knowledge Graph-Guided Multi-Agent Distillation for Reliable Industrial Question Answering with Datasets](https://mlsanity.com/paper/2510.06240/) (2025)

## Abstract (arXiv)

Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph completion. We consider triples in knowledge graphs as text sequences and introduce an innovative framework called Knowledge Graph LLM (KG-LLM) to model these triples. Our technique employs entity and relation descriptions of a triple as prompts and utilizes the response for predictions. Experiments on various benchmark knowledge graphs demonstrate that our method attains state-of-the-art performance in tasks such as triple classification and relation prediction. We also find that fine-tuning relatively smaller models (e.g., LLaMA-7B, ChatGLM-6B) outperforms recent ChatGPT and GPT-4.