# Querying in the Age of Graph Databases and Knowledge Graphs

arXiv: https://arxiv.org/abs/2106.11456 · HTML: https://mlsanity.com/paper/2106.11456/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 82 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings](https://mlsanity.com/paper/2002.05969/) (2020)
- [Embedding Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/1806.01445/) (2018)
- [The Logic of Graph Neural Networks](https://mlsanity.com/paper/2104.14624/) (2021)
- [Model Interpretability through the Lens of Computational Complexity](https://mlsanity.com/paper/2010.12265/) (2020)
- [Foundations of Modern Query Languages for Graph Databases](https://mlsanity.com/paper/1610.06264/) (2016)
- [Local Graph Clustering with Network Lasso](https://mlsanity.com/paper/2004.12199/) (2020)
- [G-CORE: A Core for Future Graph Query Languages](https://mlsanity.com/paper/1712.01550/) (2017)
- [Near Neighbor: Who is the Fairest of Them All?](https://mlsanity.com/paper/1906.02640/) (2019)
- [Fair Near Neighbor Search: Independent Range Sampling in High Dimensions](https://mlsanity.com/paper/1906.01859/) (2019)

## Built on by (most cited first, 2 of 2)

- [An epistemic approach to model uncertainty in data-graphs](https://mlsanity.com/paper/2109.14112/) (2021)
- [Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge](https://mlsanity.com/paper/2601.10485/) (2026)

## Abstract (arXiv)

Graphs have become the best way we know of representing knowledge. The computing community has investigated and developed the support for managing graphs by means of digital technology. Graph databases and knowledge graphs surface as the most successful solutions to this program. The goal of this document is to provide a conceptual map of the data management tasks underlying these developments, paying particular attention to data models and query languages for graphs.