A Meta-reasoner to Rule Them All
Yong‐Bin Kang, Shonali Priyadarsini Krishnaswamy, Yuan-Fang Li · 2014
It has been shown, both theoretically and empirically, that reasoning about large and expressive ontologies is computationally hard. Moreover, due to the different reasoning algorithms and optimisation techniques employed, each reasoner may be efficient for ontologies with different characteristics. Based on recently-developed prediction models for various reasoners for reasoning performance, we present our work in developing a meta-reasoner that automatically selects from a number of state-of-the-art OWL reasoners to achieve optimal efficiency. Our preliminary evaluation shows that the meta-reasoner significantly and consistently outperforms 6 state-of-the-art reasoners and it achieves a performance close to the hypothetical gold standard reasoner.