  With the growth of the Semantic Web in size and importance, more and more
knowledge is stored in machine-readable formats such as the Web Ontology
Language OWL. This paper outlines common approaches for efficient reasoning on
large-scale data consisting of billions ($10^9$) of triples. Therefore, OWL and
its sublanguages, as well as forward and backward chaining techniques are
presented. The WebPIE reasoner is discussed in detail as an example for forward
chaining using MapReduce for materialisation. Moreover, the QueryPIE reasoner
is presented as a backward chaining/hybrid approach which uses query rewriting.
Furthermore, an overview on other reasoners is given such as OWLIM and TrOWL.
