Applying high performance computing techniques for advanced semantic reasoning

Recent advances in the Semantic Web community have enabled processing and exploitation of semantically annotated data by a variety of modern reasoning methods. However, there is still a deep mismatch between the requirements for reasoning on a Web scale and the existing efficient reasoning algorithms over restricted subsets. Fulfilment of those requirements can be greatly achieved by applying special techniques, which enable high-performance computing architectures to be included as a part of a sustainable infrastructure for large-scale semantic reasoning, as set up within the LarKC project that aims at providing large-scale reasoning over billions of structured data in heterogeneous data sets. In this paper, the mainstream strategies for overcoming the limitations of today's reasoning systems' scalability and performance by means of applying high-performance computing are discussed.