2016/02/14 by Michael Ruster, Ruster, Michael
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Databases (cs.DB) #FOS: Computer and information sciences #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.48550/arxiv.1602.04473
openalex publication_date 2016/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 (109) 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.