We present the first principled and systematic study of the expressive power of property graph constraint languages, focused on the recent PG-Keys language, set to inform the upcoming revision of the GQL standard. To this end, we position PG-Keys within the broader landscape of existing formalisms. In particular, we compare PG-Keys with two core property graph constraint languages: Graph Functional Dependencies (GFD) and Graph Generating Dependencies (GGD). One hurdle is that these formalisms allow different kinds of graph pattern languages and data predicates. To make a fair comparison, based on their structural differences only, we first present a unifying framework. Within this framework, we consider conjunctive regular path queries (CRPQ) as graph patterns with equality and inequality predicates. We then identify well-behaved fragments, establish expressiveness inclusion, and prove separation results, yielding a complete and strict hierarchy of expressive power. The results identify precisely when PG-Keys provide strictly greater expressive power, clarifying their place among state-of-the-art property graph constraint formalisms.

翻译：我们对属性图约束语言的表达能力进行了首次系统化、原理性的研究，重点聚焦于即将影响GQL标准修订的最新PG-Keys语言。为此，我们将PG-Keys置于现有形式化方法的宏观背景中进行定位，特别是将其与两种核心属性图约束语言——图函数依赖（GFD）和图生成依赖（GGD）进行比较。研究面临的一个挑战在于这些形式化方法允许使用不同类型的图模式语言和数据谓词。为进行仅基于结构差异的公平比较，我们首先提出了一个统一框架。在此框架中，我们将带等式与不等式谓词的合取正则路径查询（CRPQ）视为图模式。随后，我们识别了行为良好的语言片段，建立了表达能力包含关系，并证明了分离性结果，从而得到一个完整且严格的表达能力层次结构。研究结果精确界定了PG-Keys在何时提供严格更强的表达能力，明确了其在当前最先进的属性图约束形式化方法体系中的定位。