Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
arXiv:2508.10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs. This work presents Rule2Text, a comprehensive framework that leverages large language models (LLMs) to generate natural language explanations for mined logical rules, thereby improving KG accessibility and usability. We conduct extensive experiments using multiple datasets, including Freebase variants (FB
The proliferation of complex knowledge graphs and the advancement of large language models create a timely opportunity to bridge the interpretability gap between structured data and human understanding.
Improving the interpretability of knowledge graphs through natural language explanations enhances their accessibility and usability, accelerating adoption and application in various domains for strategic decision-makers.
Knowledge graph outputs become more intuitive and easier to integrate into human workflows without requiring specialized expertise in logical rule interpretation.
- · AI/ML developers
- · Data scientists
- · Enterprises leveraging knowledge graphs
- · LLM providers
- · Companies relying on opaque data systems
Increased efficiency in knowledge graph development and deployment due to reduced interpretability barriers.
Broader adoption of knowledge graph technologies across industries, leading to new AI-driven applications.
The development of more sophisticated, explainable AI systems that integrate logical reasoning with natural language understanding.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI