# LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT arXiv: https://arxiv.org/abs/2307.06917 ยท HTML: https://mlsanity.com/paper/2307.06917/ ## Key facts - Cited by 42 papers in the map (data through 14 Oct 2026). - Builds on 5 of its 8 references that are in the map. - First posted on arXiv 13 Jul 2023. ## Builds on (most cited first, 5 of 5) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://mlsanity.com/paper/2302.06466/) (2023) - ["I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data](https://mlsanity.com/paper/2212.05856/) (2022) - [Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG](https://mlsanity.com/paper/2303.15113/) (2023) - [Context-based Ontology Modelling for Database: Enabling ChatGPT for Semantic Database Management](https://mlsanity.com/paper/2303.07351/) (2023) ## Built on by (most cited first, 30 of 42) - [Making Large Language Models Perform Better in Knowledge Graph Completion](https://mlsanity.com/paper/2310.06671/) (2023) - [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024) - [OntoChat: a Framework for Conversational Ontology Engineering using Language Models](https://mlsanity.com/paper/2403.05921/) (2024) - [Exploring the Potential of Large Language Models in Graph Generation](https://mlsanity.com/paper/2403.14358/) (2024) - [Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL](https://mlsanity.com/paper/2402.16567/) (2024) - [Assessing SPARQL capabilities of Large Language Models](https://mlsanity.com/paper/2409.05925/) (2024) - [Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering](https://mlsanity.com/paper/2308.16622/) (2023) - [Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance](https://mlsanity.com/paper/2405.17076/) (2024) - [Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?](https://mlsanity.com/paper/2309.17122/) (2023) - [Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation](https://mlsanity.com/paper/2405.19255/) (2024) - [Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models](https://mlsanity.com/paper/2310.05628/) (2023) - [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023) - [Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability](https://mlsanity.com/paper/2401.07868/) (2024) - [Neurosymbolic Graph Enrichment for Grounded World Models](https://mlsanity.com/paper/2411.12671/) (2024) - [Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph](https://mlsanity.com/paper/2411.19064/) (2024) - [Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation](https://mlsanity.com/paper/2412.00608/) (2024) - [On the Use of Large Language Models to Generate Capability Ontologies](https://mlsanity.com/paper/2404.17524/) (2024) - [A Survey of Link Prediction in N-ary Knowledge Graphs](https://mlsanity.com/paper/2506.08970/) (2025) - [Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting](https://mlsanity.com/paper/2408.15256/) (2024) - [LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs](https://mlsanity.com/paper/2505.13098/) (2025) - [ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities](https://mlsanity.com/paper/2510.02200/) (2025) - [Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models](https://mlsanity.com/paper/2405.14012/) (2024) - [Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning](https://mlsanity.com/paper/2410.12130/) (2024) - [Guiding Generative Storytelling with Knowledge Graphs](https://mlsanity.com/paper/2505.24803/) (2025) - [KGpipe: Generation and Evaluation of Pipelines for Data Integration into Knowledge Graphs](https://mlsanity.com/paper/2511.18364/) (2025) - [Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations](https://mlsanity.com/paper/2603.13264/) (2026) - [Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial](https://mlsanity.com/paper/2309.15074/) (2023) - [From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework](https://mlsanity.com/paper/2502.08756/) (2025) - [Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM](https://mlsanity.com/paper/2503.00309/) (2025) - [How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance](https://mlsanity.com/paper/2505.16276/) (2025) ## Abstract (arXiv) Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work. Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.