Parallelization techniques for semantic web reasoning applications

Abstract

Performance is the most critical aspect towards achieving high scalability of Semantic Web reasoning applications, and considerably limits the application areas of them. There is still a deep mismatch between the requirements for reasoning on a Web scale and performance of the existing reasoning engines. The performance limitation can be considerably reduced by utilizing such large-scale e-Infrastructures as LarKC - The Large Knowledge Collider - an experimental platform for massive distributed incomplete reasoning, which offers several innovative approaches removing the scalability barriers, in particularly, by enabling transparent access to HPC systems. Efficient utilization of such resources is facilitated by means of parallelization being the major element for accomplishing performance and scalability of semantic applications. Here we discuss application of some emerging parallelization strategies and show the benefits obtained by using such systems as LarKC.