# A Software Framework and Datasets for the Analysis of Graph Measures on RDF Graphs

arXiv: https://arxiv.org/abs/1907.01885 · HTML: https://mlsanity.com/paper/1907.01885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks](https://mlsanity.com/paper/2303.01140/) (2023)

## Abstract (arXiv)

As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. In this work, we propose two resources: (i) a software framework able to acquire, prepare, and perform a graph-based analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets from the LOD Cloud with values for 28 graph measures computed with the framework. We present a preliminary analysis based on the proposed resources and point out implications for synthetic dataset generators. Finally, we identify a set of measures, that can be used to characterize graphs in the Semantic Web.