# Common Foundations for SHACL, ShEx, and PG-Schema

arXiv: https://arxiv.org/abs/2502.01295 · HTML: https://mlsanity.com/paper/2502.01295/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [PG-Schema: Schemas for Property Graphs](https://mlsanity.com/paper/2211.10962/) (2022)
- [Deciding SHACL Shape Containment through Description Logics Reasoning (Extended Version)](https://mlsanity.com/paper/2008.13603/) (2020)
- [Reconciliation of RDF* and Property Graphs](https://mlsanity.com/paper/1409.3288/) (2014)
- [Fixpoint Semantics for Recursive SHACL](https://mlsanity.com/paper/2109.08285/) (2021)
- [The Future is Big Graphs! A Community View on Graph Processing Systems](https://mlsanity.com/paper/2012.06171/) (2020)
- [Satisfiability and Containment of Recursive SHACL](https://mlsanity.com/paper/2108.13063/) (2021)
- [WShEx: A language to describe and validate Wikibase entities](https://mlsanity.com/paper/2208.02697/) (2022)
- [Expressiveness of SHACL Features and Extensions for Full Equality and Disjointness Tests](https://mlsanity.com/paper/2212.03553/) (2022)

## Built on by (most cited first, 4 of 4)

- [Schema Generation for Large Knowledge Graphs Using Large Language Models](https://mlsanity.com/paper/2506.04512/) (2025)
- [The KG-ER Conceptual Schema Language](https://mlsanity.com/paper/2508.02548/) (2025)
- [Shapes from Examples: Foundations of Shape Learning in Recursive SHACL](https://mlsanity.com/paper/2607.27934/) (2026)
- [NL2SHACL-Bench: A Benchmark Suite for Natural Language to SHACL Translation](https://mlsanity.com/paper/2608.07530/) (2026)

## Abstract (arXiv)

Graphs have emerged as an important foundation for a variety of applications, including capturing and reasoning over factual knowledge, semantic data integration, social networks, and providing factual knowledge for machine learning algorithms. To formalise certain properties of the data and to ensure data quality, there is a need to describe the schema of such graphs. Because of the breadth of applications and availability of different data models, such as RDF and property graphs, both the Semantic Web and the database community have independently developed graph schema languages: SHACL, ShEx, and PG-Schema. Each language has its unique approach to defining constraints and validating graph data, leaving potential users in the dark about their commonalities and differences. In this paper, we provide formal, concise definitions of the core components of each of these schema languages. We employ a uniform framework to facilitate a comprehensive comparison between the languages and identify a common set of functionalities, shedding light on both overlapping and distinctive features of the three languages.