Lemming – Example-based Mimicking of Knowledge Graphs

摘要

The size of knowledge graphs used in real applications grows constantly. Predicting the performance of storage solutions for knowledge graphs w.r.t. their query performance is hence of central performance for the practical use of said storage solutions. We address this challenge by learning graph invariants of a given graph. We then use these invariants to fuel a stochastic generation model that is able to generate graphs of arbitrary sizes similar to the input graph. We evaluate our graph generator, dubbed Lemming, by comparing the performance of storage solutions on synthetic and real versions of three datasets of up to $3.4 imes 10^{6}$ triples. Our results suggest that the performance of storage solutions on synthetic data generated by Lemming reflects their performance on real data in most of the cases. The source code of Lemming is available at https://github.com/dice-group/Lemming.