The construction of Knowledge Graphs (KGs) often demands substantial manual effort and domain expertise, especially when converting structured data formats like CSV files into KGs. Recent advancements in Large Language Models (LLMs) offer promising avenues to simplify this process through prompt engineering. This study investigates various prompting strategies-zero-shot, one-shot, prompt chaining, and a hybrid approach-to enable LLMs to automate the creation of KGs from CSV files. Using a dataset containing quality metrics for 2, 026 KGs generated by KGHeartBeat, the paper assesses the performance of GPT-4o, GPT-o1 mini, Claude 3.5 Sonnet, and Gemini 1.5 pro, across different prompt configurations. The findings reveal that the hybrid approach consistently produces the most accurate and complete KGs, effectively addressing challenges related to scalability and complexity.
From Quality Reports to Knowledge Graphs: a Case Study on CSV-to-KG Transformation
Pellegrino M. A.
;Tuozzo G.
2025
Abstract
The construction of Knowledge Graphs (KGs) often demands substantial manual effort and domain expertise, especially when converting structured data formats like CSV files into KGs. Recent advancements in Large Language Models (LLMs) offer promising avenues to simplify this process through prompt engineering. This study investigates various prompting strategies-zero-shot, one-shot, prompt chaining, and a hybrid approach-to enable LLMs to automate the creation of KGs from CSV files. Using a dataset containing quality metrics for 2, 026 KGs generated by KGHeartBeat, the paper assesses the performance of GPT-4o, GPT-o1 mini, Claude 3.5 Sonnet, and Gemini 1.5 pro, across different prompt configurations. The findings reveal that the hybrid approach consistently produces the most accurate and complete KGs, effectively addressing challenges related to scalability and complexity.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
