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

## TL;DR

This paper introduces a software framework and provides an analysis of 280 RDF datasets, measuring their graph properties to better understand their structure and inform data generation and optimization techniques.

## Contribution

It presents a novel software framework for analyzing RDF graph topology and offers a comprehensive analysis of numerous datasets with insights for synthetic data generation.

## Key findings

- 280 datasets analyzed for 28 graph measures
- Identification of key measures to characterize RDF graphs
- Implications for synthetic dataset generation

## Abstract

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.