Large-Scale Reasoning with OWL

TL;DR

This paper reviews methods and tools for scalable reasoning over large OWL datasets, highlighting approaches like MapReduce-based materialisation and query rewriting for efficient Semantic Web reasoning.

Contribution

It provides a comprehensive overview of large-scale OWL reasoning techniques, including detailed discussion of WebPIE and QueryPIE reasoners, and compares various existing reasoners.

Findings

WebPIE uses MapReduce for large-scale materialisation.

QueryPIE employs query rewriting for hybrid reasoning.

Multiple reasoners like OWLIM and TrOWL are evaluated.

Abstract

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.