Expressive Power of Property Graph Constraint Languages

2026-03-10

AI Summary

This study addresses the lack of a systematic comparison of the expressive power among constraint languages for property graphs, particularly the unclear positioning of PG-Keys within the Graph Query Language (GQL) standard. To resolve this, the authors develop a unified formal framework grounded in Conjunctive Regular Path Queries (CRPQs), enriched with equality and inequality predicates, to rigorously characterize the relative expressiveness of PG-Keys, Graph Functional Dependencies (GFDs), and Graph Generating Dependencies (GGDs). The work establishes precise inclusion and separation boundaries among these three formalisms, clearly delineating the expressive advantages and applicable scope of PG-Keys. These findings provide a solid theoretical foundation for the ongoing revision of the GQL standard.

Technology Category

• Knowledge Representation and Reasoning: Description Logics
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• expressive power
• property graph constraints
• PG-Keys
• Graph Functional Dependencies
• Graph Generating Dependencies

Innovation

• PG-Keys
• expressive power
• property graph constraints
• conjunctive regular path queries
• graph dependencies