<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thorsten Liebig</style></author><author><style face="normal" font="default" size="100%">Achille Fokoue</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Distributed OWL EL Reasoning: The Story So Far</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL EL</style></keyword><keyword><style  face="normal" font="default" size="100%">Scalability</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Riva del Garda, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">1261</style></volume><pages><style face="normal" font="default" size="100%">61-76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).&lt;/p&gt;
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