发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文利用大语言模型简化图规则挖掘，通过提示生成MINE GRAPH RULE查询，降低用户对图理论和查询语言的专业要求。
AI 中文摘要
在互联数据时代，图已成为以直观格式建模复杂系统的有效抽象，尤其是属性图的兴起，为导航非直观结构提供了直观且可扩展的方式。在此背景下，图挖掘技术已被开发用于测试复杂的基于图的规则，如MINE GRAPH RULE操作符，然而，该操作符要求用户具备图理论和形式化查询语言方面的先验专业知识。在本工作中，我们提出通过展示如何轻松提示大语言模型（LLMs）来制定、优化和解释复杂的关系规则，直接生成MINE GRAPH RULE查询，从而弥合用户与图关联规则挖掘过程之间的鸿沟。
英文摘要
In the era of interconnected data, graphs have emerged as an effective abstraction for modeling complex systems in an intuitive format, especially with the rise of Property Graphs, which offer an intuitive and scalable way of navigating non-intuitive structures. In this context, graph mining techniques have been developed for testing complex graph-based rules, as the MINE GRAPH RULE operator, which, however, require users to have prior expertise both in graph theory and formal query language. In this work, we propose to bridge the gap between users and the graph-association rule-mining process by showing how Large Language Models (LLMs) can be easily prompted to formulate, refine, and interpret complex relational rules, directly producing MINE GRAPH RULE queries.
