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.