Evaluating Negation with Multi-way Joins Accelerates Class Expression Learning

Abstract

Class expression learning based on refinement operators is a popular family of explainable machine learning approaches for RDF knowledge graphs with ontologies in description logics. However, most implementations of this paradigm fail to scale to the large knowledge graphs found on the Web. One common bottleneck of these implementations is the instance retrieval function. We address this drawback by introducing an algorithm inspired by worst-case optimal multi-way joins for the evaluation of SPARQL queries that correspond to $\mathcal {ALC}$ class expressions. The main characteristic of our algorithm is the inclusion of negation, which is prominent in SPARQL queries generated from $\mathcal {ALC}$ class expressions, in multi-way join plans. We evaluate the implementation of our approach on five benchmark datasets against four state-of-the-art graph storage solutions for RDF knowledge graphs. The results of our extensive evaluation show that our approach outperforms its competition across all datasets and that it is the only one able to scale to large datasets. With our approach, we enable learning algorithms to retrieve information from Web-scale knowledge graphs, hence making ante-hoc explainable machine learning easier to deploy on the Semantic Web.