As applications based on semantic web technologies enter the mainstream, there is a need to provide highly efficient ontology reasoning over large Aboxes. A common approach to achieving scalability is to build reasoners for DL subsets (e.g., the EL family of languages, DL-Lite, DLP, or OWL-Prime). However, the proliferation of DL subsets runs counter to standardization efforts. In this paper, we present a hybrid approach which combines a fast, incomplete reasoning algorithm with a slower complete reasoning algorithm to handle the more expressive features of DL. Our approach works for SHIN. We demonstrate the effectiveness of this approach on large datasets (30-60 million assertions), where we show that performance on this hybrid approach provides significant performance gains (an average of 15 mins per query compared to 100 mins) without sacrificing completeness or expressivity.
By: Julian Dolby, Achille Fokoue, Aditya Kalyanpur, Li Ma, Chintan Patel, Edith Schonberg, Kavitha Srinivas, Xing Zhi Sun
Published in: RC24562 in 2008
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