Predicting Performance of OWL Reasoners: Locally or Globally?

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 fea-tures 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 cor-pus), selects suitable, small ontology subsets, and ex-trapolates their performance measurements to the whole ontology. Our results show that this simple idea leads to accurate performance predictions, comparable or su-perior to global approaches. Our second contribution concerns ontology features: we are the first to investi-gate intercorrelation of ontology features using Princi-pal Component Analysis (PCA). We report that extract-ing multiple features- as global approaches do- makes surprisingly little difference for performance prediction. In fact, it turns out that the ontologies in two major cor-pora basically only differ in one or two features