@conference {156,
	title = {Distributed and Scalable OWL EL Reasoning},
	booktitle = {Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015) },
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	address = {Portoroz, Slovenia},
	abstract = {<p class="rtejustify">OWL 2 EL is one of the tractable proles of the Web Ontology\&nbsp;Language (OWL) which is a W3C-recommended standard. OWL 2\&nbsp;EL provides sucient expressivity to model large biomedical ontologies\&nbsp;as well as streaming data such as trac, while at the same time allows\&nbsp;for ecient reasoning services. Existing reasoners for OWL 2 EL, however,\&nbsp;use only a single machine and are thus constrained by memory and\&nbsp;computational power. At the same time, the automated generation of\&nbsp;ontological information from streaming data and text can lead to very\&nbsp;large ontologies which can exceed the capacities of these reasoners. We\&nbsp;thus describe a distributed reasoning system that scales well using a cluster\&nbsp;of commodity machines. We also apply our system to a use case on\&nbsp;city trac data and show that it can handle volumes which cannot be\&nbsp;handled by current single machine reasoners.</p>
},
	keywords = {DistEL, Distributed Reasoning, Ontology Classification, OWL EL},
	author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti and Freddy L{\'e}cu{\'e}}
}
@conference {160,
	title = {Very Large Scale OWL Reasoning through Distributed Computation},
	booktitle = {11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II},
	volume = {7650},
	year = {2012},
	pages = {407{\textendash}414},
	publisher = {Springer},
	organization = {Springer},
	address = {Boston, MA, USA},
	abstract = {<p class="rtejustify">Due to recent developments in reasoning algorithms of the\&nbsp;various OWL profiles, the classification time for an ontology has come\&nbsp;down drastically. For all of the popular reasoners, in order to process\&nbsp;an ontology, an implicit assumption is that the ontology should fit in\&nbsp;primary memory. The memory requirements for a reasoner are already\&nbsp;quite high, and considering the ever increasing size of the data to be\&nbsp;processed and the goal of making reasoning Web scale, this assumption\&nbsp;becomes overly restrictive. In our work, we study several distributed\&nbsp;classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.</p>
},
	keywords = {Distributed Reasoning, Ontology Classification, OWL EL},
	doi = {10.1007/978-3-642-35173-0_30},
	url = {http://dx.doi.org/10.1007/978-3-642-35173-0_30},
	author = {Raghava Mutharaju},
	editor = {Philippe Cudr{\'e}-Mauroux and Jeff Heflin and Evren Sirin and Tania Tudorache and J{\'e}r{\^o}me Euzenat and Manfred Hauswirth and Josiane Xavier Parreira and James A. Hendler and Guus Schreiber and Abraham Bernstein and Eva Blomqvist}
}
