Year: 2016
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Ruster, Michael
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2285041248