This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. We present a new characterisation of GED satisfaction, and devise a depth-first search strategy to traverse the search space of candidate rules efficiently. Further, we perform experiments to demonstrate the feasibility and scalability of our solution, FASTAGEDS, with three real-world graphs.

翻译：本文研究属性图中近似规则的发现。我们提出了一种具有语义意义的误差度量，用于挖掘几乎成立的图实体依赖（GEDs），以容忍真实世界图中存在的错误和不一致性。我们给出了GED满足性的新表征，并设计了一种深度优先搜索策略来高效遍历候选规则的搜索空间。此外，我们使用三个真实世界图进行了实验，以证明我们的解决方案FASTAGEDS的可行性和可扩展性。