On scalable RDFS reasoning using a hybrid approach

Reasoning is a vital ability for semantic web applications since they aim to understand and interpret the data on the World Wide Web. However, reasoning of large data sets is one of the challenges facing semantic web applications. In this paper, we present new approaches for scalable Resource Description Framework Schema (RDFS) reasoning. Our RDFS specific term-based partitioning algorithm determines required schema elements for each data partition while eliminating the data partitions that will not produce any inferences. With the two-level partitioning approach, we are able to carry out reasoning with limited resources. In our hybrid approach, we integrate two previously mentioned methods to benefit from the advantages of both. In the experimental tests we achieve linear speedups for reasoning times with the proposed hybrid approach. These algorithms and methods presented in the paper enable RDFS-level reasoning of large data sets with limited resources, and they together build up a scalable distributed reasoning approach.