Large-Scale Reasoning with OWL
This work addresses the challenge of scalable reasoning for Semantic Web applications, but it is incremental as it reviews existing methods without introducing new paradigms.
The paper tackles the problem of efficient reasoning on large-scale Semantic Web data with billions of triples, presenting common approaches including forward and backward chaining techniques, and discusses specific reasoners like WebPIE and QueryPIE as examples.
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.