Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules

The article introduces Rule2Text, a framework that utilizes large language models (LLMs) to generate natural language explanations for complex logical rules derived from knowledge graphs (KGs). Extensive experiments were conducted using datasets like Freebase variants and ogbl-biokg, employing models such as Gemini 2.0 Flash and the open-source Zephyr model, which was fine-tuned for improved explanation quality. This framework enhances KG usability by providing interpretable outputs, making it valuable for practitioners aiming to improve human understanding of KGs through LLM-generated explanations.