Efficient reasoning on large SHIN Aboxes in relational databases

As applications based on semantic web technologies enter the mainstream, there is a need to provide highly efficient ontology reasoning over large Aboxes. However, achieving sufficient scalability is still a challenge, especially for expressive ontologies. 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), including a clinical-trial patient matching application, where we show significant performance gains (an average of 15 mins per query compared to 100 mins) without sacrificing completeness or expressivity.