Predicting Performance of OWL Reasoners: Locally or Globally?

ABSTRACT. We propose a novel approach to performance prediction of OWL reasoners. The existing strategies take a view of an entire ontology corpus: they extract multiple features from the ontologies in the corpus and use them for training machine learning models. We call these global approaches. In contrast, our approach is a local one: it examines a single ontology (independent of any corpus), selects suitable, small ontology subsets, and extrapolates their performance measurements to the whole ontology. Our results show that this simple idea leads to accurate performance predictions, comparable or superior to global approaches. Our second contribution concerns ontology features: we are the first to investigate intercorrelation of ontology features using Principal Component Analysis (PCA). We report that extracting multiple features - as global approaches do - makes surprisingly little difference for performance prediction. In fact, it turns out that the ontologies in two major corpora basically only differ in one or two features.