# G-CORE: A Core for Future Graph Query Languages

arXiv: https://arxiv.org/abs/1712.01550 · HTML: https://mlsanity.com/paper/1712.01550/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2017.

## Built on by (most cited first, 13 of 13)

- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Knowledge Graphs Querying](https://mlsanity.com/paper/2305.14485/) (2023)
- [Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking](https://mlsanity.com/paper/2107.02865/) (2021)
- [A Logical View of GNN-Style Computation and the Role of Activation Functions](https://mlsanity.com/paper/2512.19332/) (2025)
- [Querying in the Age of Graph Databases and Knowledge Graphs](https://mlsanity.com/paper/2106.11456/) (2021)
- [From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries (Extended Version)](https://mlsanity.com/paper/2402.08509/) (2024)
- [ProGS: Property Graph Shapes Language (Extended Version)](https://mlsanity.com/paper/2107.05566/) (2021)
- [On the complexity of finding set repairs for data-graphs](https://mlsanity.com/paper/2206.07504/) (2022)
- [Shape Fragments](https://mlsanity.com/paper/2112.11796/) (2021)
- [Integrating connection search in graph queries](https://mlsanity.com/paper/2208.04802/) (2022)
- [Higher-Order Graph Databases](https://mlsanity.com/paper/2506.19661/) (2025)
- [Transforming Shape Schemas with Composable Property-Graph Queries (Extended Version)](https://mlsanity.com/paper/2606.14309/) (2026)

## Abstract (arXiv)

We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity.